Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
3,200 | On the Conditioning of the Spherical Harmonic Matrix for Spatial Audio Applications | eess.AS | In this paper, we attempt to study the conditioning of the Spherical Harmonic
Matrix (SHM), which is widely used in the discrete, limited order orthogonal
representation of sound fields. SHM's has been widely used in the audio
applications like spatial sound reproduction using loudspeakers, orthogonal
representation of... | electrics |
3,201 | Singing voice correction using canonical time warping | eess.AS | Expressive singing voice correction is an appealing but challenging problem.
A robust time-warping algorithm which synchronizes two singing recordings can
provide a promising solution. We thereby propose to address the problem by
canonical time warping (CTW) which aligns amateur singing recordings to
professional ones.... | electrics |
3,202 | Raga Identification using Repetitive Note Patterns from prescriptive notations of Carnatic Music | eess.AS | Carnatic music, a form of Indian Art Music, has relied on an oral tradition
for transferring knowledge across several generations. Over the last two
hundred years, the use of prescriptive notations has been adopted for learning,
sight-playing and sight-singing. Prescriptive notations offer generic
guidelines for a raga... | electrics |
3,203 | Enhancement of Noisy Speech Exploiting an Exponential Model Based Threshold and a Custom Thresholding Function in Perceptual Wavelet Packet Domain | eess.AS | For enhancement of noisy speech, a method of threshold determination based on
modeling of Teager energy (TE) operated perceptual wavelet packet (PWP)
coefficients of the noisy speech by exponential distribution is presented. A
custom thresholding function based on the combination of mu-law and semisoft
thresholding fun... | electrics |
3,204 | Precise Detection of Speech Endpoints Dynamically: A Wavelet Convolution based approach | eess.AS | Precise detection of speech endpoints is an important factor which affects
the performance of the systems where speech utterances need to be extracted
from the speech signal such as Automatic Speech Recognition (ASR) system.
Existing endpoint detection (EPD) methods mostly uses Short-Term Energy (STE),
Zero-Crossing Ra... | electrics |
3,205 | Simulating dysarthric speech for training data augmentation in clinical speech applications | eess.AS | Training machine learning algorithms for speech applications requires large,
labeled training data sets. This is problematic for clinical applications where
obtaining such data is prohibitively expensive because of privacy concerns or
lack of access. As a result, clinical speech applications are typically
developed usi... | electrics |
3,206 | Angular Softmax Loss for End-to-end Speaker Verification | eess.AS | End-to-end speaker verification systems have received increasing interests.
The traditional i-vector approach trains a generative model (basically a
factor-analysis model) to extract i-vectors as speaker embeddings. In contrast,
the end-to-end approach directly trains a discriminative model (often a neural
network) to ... | electrics |
3,207 | RTF-Based Binaural MVDR Beamformer Exploiting an External Microphone in a Diffuse Noise Field | eess.AS | Besides suppressing all undesired sound sources, an important objective of a
binaural noise reduction algorithm for hearing devices is the preservation of
the binaural cues, aiming at preserving the spatial perception of the acoustic
scene. A well-known binaural noise reduction algorithm is the binaural minimum
varianc... | electrics |
3,208 | Independent Low-Rank Matrix Analysis Based on Time-Variant Sub-Gaussian Source Model | eess.AS | Independent low-rank matrix analysis (ILRMA) is a fast and stable method for
blind audio source separation. Conventional ILRMAs assume time-variant
(super-)Gaussian source models, which can only represent signals that follow a
super-Gaussian distribution. In this paper, we focus on ILRMA based on a
generalized Gaussian... | electrics |
3,209 | Advancing Multi-Accented LSTM-CTC Speech Recognition using a Domain Specific Student-Teacher Learning Paradigm | eess.AS | Non-native speech causes automatic speech recognition systems to degrade in
performance. Past strategies to address this challenge have considered model
adaptation, accent classification with a model selection, alternate
pronunciation lexicon, etc. In this study, we consider a recurrent neural
network (RNN) with connec... | electrics |
3,210 | Evaluating MCC-PHAT for the LOCATA Challenge - Task 1 and Task 3 | eess.AS | This report presents test results for the \mbox{LOCATA} challenge
\cite{lollmann2018locata} using the recently developed MCC-PHAT (multichannel
cross correlation - phase transform) sound source localization method. The
specific tasks addressed are respectively the localization of a single static
and a single moving spe... | electrics |
3,211 | Error Reduction Network for DBLSTM-based Voice Conversion | eess.AS | So far, many of the deep learning approaches for voice conversion produce
good quality speech by using a large amount of training data. This paper
presents a Deep Bidirectional Long Short-Term Memory (DBLSTM) based voice
conversion framework that can work with a limited amount of training data. We
propose to implement ... | electrics |
3,212 | Concatenated Identical DNN (CI-DNN) to Reduce Noise-Type Dependence in DNN-Based Speech Enhancement | eess.AS | Estimating time-frequency domain masks for speech enhancement using deep
learning approaches has recently become a popular field of research. In this
paper, we propose a mask-based speech enhancement framework by using
concatenated identical deep neural networks (CI-DNNs). The idea is that a
single DNN is trained under... | electrics |
3,213 | A Proper version of Synthesis-based Sparse Audio Declipper | eess.AS | Methods based on sparse representation have found great use in the recovery
of audio signals degraded by clipping. The state of the art in declipping has
been achieved by the SPADE algorithm by Kiti\'c et. al. (LVA/ICA2015). Our
recent study (LVA/ICA2018) has shown that although the original S-SPADE can be
improved suc... | electrics |
3,214 | Speech Coding, Speech Interfaces and IoT - Opportunities and Challenges | eess.AS | Recent speech and audio coding standards such as 3GPP Enhanced Voice Services
match the foreseeable needs and requirements in transmission of speech and
audio, when using current transmission infrastructure and applications. Trends
in Internet-of-Things technology and development in personal digital assistants
(PDAs) h... | electrics |
3,215 | Building and Evaluation of a Real Room Impulse Response Dataset | eess.AS | This paper presents BUT ReverbDB - a dataset of real room impulse responses
(RIR), background noises and re-transmitted speech data. The retransmitted data
includes LibriSpeech test-clean, 2000 HUB5 English evaluation and part of 2010
NIST Speaker Recognition Evaluation datasets. We provide a detailed description
of RI... | electrics |
3,216 | Non linear time compression of clear and normal speech at high rates | eess.AS | We compare a series of time compression methods applied to normal and clear
speech. First we evaluate a linear (uniform) method applied to these styles as
well as to naturally-produced fast speech. We found, in line with the
literature, that unprocessed fast speech was less intelligible than linearly
compressed normal ... | electrics |
3,217 | Speaker Verification By Partial AUC Optimization With Mahalanobis Distance Metric Learning | eess.AS | Receiver operating characteristic (ROC) and detection error tradeoff (DET)
curves are two widely used evaluation metrics for speaker verification. They
are equivalent since the latter can be obtained by transforming the former's
true positive y-axis to false negative y-axis and then re-scaling both axes by
a probit ope... | electrics |
3,218 | Overlap-Add Windows with Maximum Energy Concentration for Speech and Audio Processing | eess.AS | Processing of speech and audio signals with time-frequency representations
require windowing methods which allow perfect reconstruction of the original
signal and where processing artifacts have a predictable behavior. The most
common approach for this purpose is overlap-add windowing, where signal
segments are windowe... | electrics |
3,219 | Active Acoustic Source Tracking Exploiting Particle Filtering and Monte Carlo Tree Search | eess.AS | In this paper, we address the task of active acoustic source tracking as part
of robotic path planning. It denotes the planning of sequences of robotic
movements to enhance tracking results of acoustic sources, e.g., talking
humans, by fusing observations from multiple positions. Essentially, two
strategies are possibl... | electrics |
3,220 | Irrelevant speech effect in open plan offices: A laboratory study | eess.AS | It seems now accepted that speech noise in open plan offices is the main
source of discomfort for employees. This work follows a series of studies
conducted at INRS France and INSA Lyon based on Hongisto's theoretical model
(2005) linking the Decrease in Performance (DP) and the Speech Transmission
Index (STI). This mo... | electrics |
3,221 | USTCSpeech System for VOiCES from a Distance Challenge 2019 | eess.AS | This document describes the speaker verification systems developed in the
Speech lab at the University of Science and Technology of China (USTC) for the
VOiCES from a Distance Challenge 2019. We develop the system for the Fixed
Condition on two public corpus, VoxCeleb and SITW. The frameworks of our
systems are based o... | electrics |
3,222 | An End-to-End Approach to Automatic Speech Assessment for Cantonese-speaking People with Aphasia | eess.AS | Conventional automatic assessment of pathological speech usually follows two
main steps: (1) extraction of pathology-specific features; (2) classification
or regression on extracted features. Given the great variety of speech and
language disorders, feature design is never a straightforward task, and yet it
is most cru... | electrics |
3,223 | Room Geometry Estimation from Room Impulse Responses using Convolutional Neural Networks | eess.AS | We describe a new method to estimate the geometry of a room given room
impulse responses. The method utilises convolutional neural networks to
estimate the room geometry and uses the mean square error as the loss function.
In contrast to existing methods, we do not require the position or distance of
sources or receive... | electrics |
3,224 | Progressive Speech Enhancement with Residual Connections | eess.AS | This paper studies the Speech Enhancement based on Deep Neural Networks. The
proposed architecture gradually follows the signal transformation during
enhancement by means of a visualization probe at each network block. Alongside
the process, the enhancement performance is visually inspected and evaluated in
terms of re... | electrics |
3,225 | Leveraging native language information for improved accented speech recognition | eess.AS | Recognition of accented speech is a long-standing challenge for automatic
speech recognition (ASR) systems, given the increasing worldwide population of
bi-lingual speakers with English as their second language. If we consider
foreign-accented speech as an interpolation of the native language (L1) and
English (L2), usi... | electrics |
3,226 | Latent Class Model with Application to Speaker Diarization | eess.AS | In this paper, we apply a latent class model (LCM) to the task of speaker
diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in
that it uses soft information and avoids premature hard decisions in its
iterations. In contrast to the VB method, which is based on a generative model,
LCM provides ... | electrics |
3,227 | Semi-Supervised Speech Emotion Recognition with Ladder Networks | eess.AS | Speech emotion recognition (SER) systems find applications in various fields
such as healthcare, education, and security and defense. A major drawback of
these systems is their lack of generalization across different conditions. This
problem can be solved by training models on large amounts of labeled data from
the tar... | electrics |
3,228 | Binaural LCMV Beamforming with Partial Noise Estimation | eess.AS | Besides reducing undesired sources (interfering sources and background
noise), another important objective of a binaural beamforming algorithm is to
preserve the spatial impression of the acoustic scene, which can be achieved by
preserving the binaural cues of all sound sources. While the binaural minimum
variance dist... | electrics |
3,229 | Measuring the Effectiveness of Voice Conversion on Speaker Identification and Automatic Speech Recognition Systems | eess.AS | This paper evaluates the effectiveness of a Cycle-GAN based voice converter
(VC) on four speaker identification (SID) systems and an automated speech
recognition (ASR) system for various purposes. Audio samples converted by the
VC model are classified by the SID systems as the intended target at up to 46%
top-1 accurac... | electrics |
3,230 | The DKU-SMIIP System for NIST 2018 Speaker Recognition Evaluation | eess.AS | In this paper, we present the system submission for the NIST 2018 Speaker
Recognition Evaluation by DKU Speech and Multi-Modal Intelligent Information
Processing (SMIIP) Lab. We explore various kinds of state-of-the-art front-end
extractors as well as back-end modeling for text-independent speaker
verifications. Our su... | electrics |
3,231 | The DKU System for the Speaker Recognition Task of the 2019 VOiCES from a Distance Challenge | eess.AS | In this paper, we present the DKU system for the speaker recognition task of
the VOiCES from a distance challenge 2019. We investigate the whole system
pipeline for the far-field speaker verification, including data pre-processing,
short-term spectral feature representation, utterance-level speaker modeling,
back-end s... | electrics |
3,232 | Localization Uncertainty in Time-Amplitude Stereophonic Reproduction | eess.AS | This article studies the effects of inter-channel time and level differences
in stereophonic reproduction on perceived localization uncertainty, which is
defined as how difficult it is for a listener to tell where a sound source is
located. Towards this end, a computational model of localization uncertainty is
proposed... | electrics |
3,233 | Black-box Attacks on Automatic Speaker Verification using Feedback-controlled Voice Conversion | eess.AS | Automatic speaker verification (ASV) systems in practice are greatly
vulnerable to spoofing attacks. The latest voice conversion technologies are
able to produce perceptually natural sounding speech that mimics any target
speakers. However, the perceptual closeness to a speaker's identity may not be
enough to deceive a... | electrics |
3,234 | A Modularized Neural Network with Language-Specific Output Layers for Cross-lingual Voice Conversion | eess.AS | This paper presents a cross-lingual voice conversion framework that adopts a
modularized neural network. The modularized neural network has a common input
structure that is shared for both languages, and two separate output modules,
one for each language. The idea is motivated by the fact that phonetic systems
of langu... | electrics |
3,235 | Objective Human Affective Vocal Expression Detection and Automatic Classification with Stochastic Models and Learning Systems | eess.AS | This paper presents a widespread analysis of affective vocal expression
classification systems. In this study, state-of-the-art acoustic features are
compared to two novel affective vocal prints for the detection of emotional
states: the Hilbert-Huang-Hurst Coefficients (HHHC) and the vector of index of
non-stationarit... | electrics |
3,236 | Cross lingual transfer learning for zero-resource domain adaptation | eess.AS | We propose a method for zero-resource domain adaptation of DNN acoustic
models, for use in low-resource situations where the only in-language training
data available may be poorly matched to the intended target domain. Our method
uses a multi-lingual model in which several DNN layers are shared between
languages. This ... | electrics |
3,237 | Multi-Talker MVDR Beamforming Based on Extended Complex Gaussian Mixture Model | eess.AS | In this letter, we present a novel multi-talker minimum variance
distortionless response (MVDR) beamforming as the front-end of an automatic
speech recognition (ASR) system in a dinner party scenario. The CHiME-5 dataset
is selected to evaluate our proposal for overlapping multi-talker scenario with
severe noise. A det... | electrics |
3,238 | Multi-channel Time-Varying Covariance Matrix Model for Late Reverberation Reduction | eess.AS | In this paper, a multi-channel time-varying covariance matrix model for late
reverberation reduction is proposed. Reflecting that variance of the late
reverberation is time-varying and it depends on past speech source variance,
the proposed model is defined as convolution of a speech source variance with a
multi-channe... | electrics |
3,239 | Frequency-Sliding Generalized Cross-Correlation: A Sub-band Time Delay Estimation Approach | eess.AS | The generalized cross correlation (GCC) is regarded as the most popular
approach for estimating the time difference of arrival (TDOA) between the
signals received at two sensors. Time delay estimates are obtained by
maximizing the GCC output, where the direct-path delay is usually observed as a
prominent peak. Moreover... | electrics |
3,240 | BUT System Description for DIHARD Speech Diarization Challenge 2019 | eess.AS | This paper describes the systems developed by the BUT team for the four
tracks of the second DIHARD speech diarization challenge. For tracks 1 and 2
the systems were based on performing agglomerative hierarchical clustering
(AHC) over x-vectors, followed by the Bayesian Hidden Markov Model (HMM) with
eigenvoice priors ... | electrics |
3,241 | Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models | eess.AS | End-to-end models are an attractive new approach to spoken language
understanding (SLU) in which the meaning of an utterance is inferred directly
from the raw audio without employing the standard pipeline composed of a
separately trained speech recognizer and natural language understanding module.
The downside of end-t... | electrics |
3,242 | GCI detection from raw speech using a fully-convolutional network | eess.AS | Glottal Closure Instants (GCI) detection consists in automatically detecting
temporal locations of most significant excitation of the vocal tract from the
speech signal. It is used in many speech analysis and processing applications,
and various algorithms have been proposed for this purpose. Recently, new
approaches u... | electrics |
3,243 | QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions | eess.AS | We propose a new end-to-end neural acoustic model for automatic speech
recognition. The model is composed of multiple blocks with residual connections
between them. Each block consists of one or more modules with 1D time-channel
separable convolutional layers, batch normalization, and ReLU layers. It is
trained with CT... | electrics |
3,244 | End-to-end architectures for ASR-free spoken language understanding | eess.AS | Spoken Language Understanding (SLU) is the problem of extracting the meaning
from speech utterances. It is typically addressed as a two-step problem, where
an Automatic Speech Recognition (ASR) model is employed to convert speech into
text, followed by a Natural Language Understanding (NLU) model to extract
meaning fro... | electrics |
3,245 | End-to-end Domain-Adversarial Voice Activity Detection | eess.AS | Voice activity detection is the task of detecting speech regions in a given
audio stream or recording. First, we design a neural network combining
trainable filters and recurrent layers to tackle voice activity detection
directly from the waveform. Experiments on the challenging DIHARD dataset show
that the proposed en... | electrics |
3,246 | Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings | eess.AS | While speaker adaptation for end-to-end speech synthesis using speaker
embeddings can produce good speaker similarity for speakers seen during
training, there remains a gap for zero-shot adaptation to unseen speakers. We
investigate multi-speaker modeling for end-to-end text-to-speech synthesis and
study the effects of... | electrics |
3,247 | Learning deep representations by multilayer bootstrap networks for speaker diarization | eess.AS | The performance of speaker diarization is strongly affected by its clustering
algorithm at the test stage. However, it is known that clustering algorithms
are sensitive to random noises and small variations, particularly when the
clustering algorithms themselves suffer some weaknesses, such as bad local
minima and prio... | electrics |
3,248 | Analyzing the impact of speaker localization errors on speech separation for automatic speech recognition | eess.AS | We investigate the effect of speaker localization on the performance of
speech recognition systems in a multispeaker, multichannel environment. Given
the speaker location information, speech separation is performed in three
stages. In the first stage, a simple delay-and-sum (DS) beamformer is used to
enhance the signal... | electrics |
3,249 | SLOGD: Speaker LOcation Guided Deflation approach to speech separation | eess.AS | Speech separation is the process of separating multiple speakers from an
audio recording. In this work we propose to separate the sources using a
Speaker LOcalization Guided Deflation (SLOGD) approach wherein we estimate the
sources iteratively. In each iteration we first estimate the location of the
speaker and use it... | electrics |
3,250 | Overlapped speech recognition from a jointly learned multi-channel neural speech extraction and representation | eess.AS | We propose an end-to-end joint optimization framework of a multi-channel
neural speech extraction and deep acoustic model without mel-filterbank (FBANK)
extraction for overlapped speech recognition. First, based on a multi-channel
convolutional TasNet with STFT kernel, we unify the multi-channel target speech
enhanceme... | electrics |
3,251 | Modeling of Rakugo Speech and Its Limitations: Toward Speech Synthesis That Entertains Audiences | eess.AS | We have been investigating rakugo speech synthesis as a challenging example
of speech synthesis that entertains audiences. Rakugo is a traditional Japanese
form of verbal entertainment similar to a combination of one-person stand-up
comedy and comic storytelling and is popular even today. In rakugo, a performer
plays m... | electrics |
3,252 | Deep neural networks for emotion recognition combining audio and transcripts | eess.AS | In this paper, we propose to improve emotion recognition by combining
acoustic information and conversation transcripts. On the one hand, an LSTM
network was used to detect emotion from acoustic features like f0, shimmer,
jitter, MFCC, etc. On the other hand, a multi-resolution CNN was used to detect
emotion from word ... | electrics |
3,253 | Mask-dependent Phase Estimation for Monaural Speaker Separation | eess.AS | Speaker separation refers to isolating speech of interest in a multi-talker
environment. Most methods apply real-valued Time-Frequency (T-F) masks to the
mixture Short-Time Fourier Transform (STFT) to reconstruct the clean speech.
Hence there is an unavoidable mismatch between the phase of the reconstruction
and the or... | electrics |
3,254 | Signal-Adaptive and Perceptually Optimized Sound Zones with Variable Span Trade-Off Filters | eess.AS | Creating sound zones has been an active research field since the idea was
first proposed. So far, most sound zone control methods rely on either an
optimization of physical metrics such as acoustic contrast and signal
distortion or a mode decomposition of the desired sound field. By using these
types of methods, approx... | electrics |
3,255 | Sound event detection via dilated convolutional recurrent neural networks | eess.AS | Convolutional recurrent neural networks (CRNNs) have achieved
state-of-the-art performance for sound event detection (SED). In this paper, we
propose to use a dilated CRNN, namely a CRNN with a dilated convolutional
kernel, as the classifier for the task of SED. We investigate the effectiveness
of dilation operations w... | electrics |
3,256 | Cross-lingual Multi-speaker Text-to-speech Synthesis for Voice Cloning without Using Parallel Corpus for Unseen Speakers | eess.AS | We investigate a novel cross-lingual multi-speaker text-to-speech synthesis
approach for generating high-quality native or accented speech for
native/foreign seen/unseen speakers in English and Mandarin. The system
consists of three separately trained components: an x-vector speaker encoder, a
Tacotron-based synthesize... | electrics |
3,257 | Automatic prediction of suicidal risk in military couples using multimodal interaction cues from couples conversations | eess.AS | Suicide is a major societal challenge globally, with a wide range of risk
factors, from individual health, psychological and behavioral elements to
socio-economic aspects. Military personnel, in particular, are at especially
high risk. Crisis resources, while helpful, are often constrained by access to
clinical visits ... | electrics |
3,258 | Multi-Source Direction-of-Arrival Estimation Using Improved Estimation Consistency Method | eess.AS | We address the problem of estimating direction-of-arrivals (DOAs) for
multiple acoustic sources in a reverberant environment using a spherical
microphone array. It is well-known that multi-source DOA estimation is
challenging in the presence of room reverberation, environmental noise and
overlapping sources. In this wo... | electrics |
3,259 | Attention-based ASR with Lightweight and Dynamic Convolutions | eess.AS | End-to-end (E2E) automatic speech recognition (ASR) with sequence-to-sequence
models has gained attention because of its simple model training compared with
conventional hidden Markov model based ASR. Recently, several studies report
the state-of-the-art E2E ASR results obtained by Transformer. Compared to
recurrent ne... | electrics |
3,260 | Attention-based gated scaling adaptative acoustic model for ctc-based speech recognition | eess.AS | In this paper, we propose a novel adaptive technique that uses an
attention-based gated scaling (AGS) scheme to improve deep feature learning for
connectionist temporal classification (CTC) acoustic modeling. In AGS, the
outputs of each hidden layer of the main network are scaled by an auxiliary
gate matrix extracted f... | electrics |
3,261 | A Memory Augmented Architecture for Continuous Speaker Identification in Meetings | eess.AS | We introduce and analyze a novel approach to the problem of speaker
identification in multi-party recorded meetings. Given a speech segment and a
set of available candidate profiles, we propose a novel data-driven way to
model the distance relations between them, aiming at identifying the speaker
label corresponding to... | electrics |
3,262 | Interpretable Filter Learning Using Soft Self-attention For Raw Waveform Speech Recognition | eess.AS | Speech recognition from raw waveform involves learning the spectral
decomposition of the signal in the first layer of the neural acoustic model
using a convolution layer. In this work, we propose a raw waveform
convolutional filter learning approach using soft self-attention. The acoustic
filter bank in the proposed mo... | electrics |
3,263 | Noise dependent Super Gaussian-Coherence based dual microphone Speech Enhancement for hearing aid application using smartphone | eess.AS | In this paper, the coherence between speech and noise signals is used to
obtain a Speech Enhancement (SE) gain function, in combination with a Super
Gaussian Joint Maximum a Posteriori (SGJMAP) single microphone SE gain
function. The proposed SE method can be implemented on a smartphone that works
as an assistive devic... | electrics |
3,264 | Phase-Aware Speech Enhancement with a Recurrent Two Stage Net work | eess.AS | We propose a neural network-based speech enhancement (SE) method called the
phase-aware recurrent two stage network (rTSN). The rTSN is an extension of our
previously proposed two stage network (TSN) framework. This TSN framework was
equipped with a boosting strategy (BS) that initially estimates the multiple
base pred... | electrics |
3,265 | Source coding of audio signals with a generative model | eess.AS | We consider source coding of audio signals with the help of a generative
model. We use a construction where a waveform is first quantized, yielding a
finite bitrate representation. The waveform is then reconstructed by random
sampling from a model conditioned on the quantized waveform. The proposed
coding scheme is the... | electrics |
3,266 | Improving LPCNet-based Text-to-Speech with Linear Prediction-structured Mixture Density Network | eess.AS | In this paper, we propose an improved LPCNet vocoder using a linear
prediction (LP)-structured mixture density network (MDN). The recently proposed
LPCNet vocoder has successfully achieved high-quality and lightweight speech
synthesis systems by combining a vocal tract LP filter with a WaveRNN-based
vocal source (i.e.,... | electrics |
3,267 | Multitask Learning with Capsule Networks for Speech-to-Intent Applications | eess.AS | Voice controlled applications can be a great aid to society, especially for
physically challenged people. However this requires robustness to all kinds of
variations in speech. A spoken language understanding system that learns from
interaction with and demonstrations from the user, allows the use of such a
system in d... | electrics |
3,268 | Multi-Branch Learning for Weakly-Labeled Sound Event Detection | eess.AS | There are two sub-tasks implied in the weakly-supervised SED: audio tagging
and event boundary detection. Current methods which combine multi-task learning
with SED requires annotations both for these two sub-tasks. Since there are
only annotations for audio tagging available in weakly-supervised SED, we
design multipl... | electrics |
3,269 | Controllable Sequence-To-Sequence Neural TTS with LPCNET Backend for Real-time Speech Synthesis on CPU | eess.AS | State-of-the-art sequence-to-sequence acoustic networks, that convert a
phonetic sequence to a sequence of spectral features with no explicit prosody
prediction, generate speech with close to natural quality, when cascaded with
neural vocoders, such as Wavenet. However, the combined system is typically too
heavy for re... | electrics |
3,270 | An LSTM Based Architecture to Relate Speech Stimulus to EEG | eess.AS | Modeling the relationship between natural speech and a recorded
electroencephalogram (EEG) helps us understand how the brain processes speech
and has various applications in neuroscience and brain-computer interfaces. In
this context, so far mainly linear models have been used. However, the decoding
performance of the ... | electrics |
3,271 | Lightweight Online Separation of the Sound Source of Interest through BLSTM-Based Binary Masking | eess.AS | Online audio source separation has been an important part of auditory scene
analysis and robot audition. The main type of technique to carry this out,
because of its online capabilities, has been spatial filtering (or
beamforming), where it is assumed that the location (mainly, the direction of
arrival; DOA) of the sou... | electrics |
3,272 | Multitask Learning and Multistage Fusion for Dimensional Audiovisual Emotion Recognition | eess.AS | Due to its ability to accurately predict emotional state using multimodal
features, audiovisual emotion recognition has recently gained more interest
from researchers. This paper proposes two methods to predict emotional
attributes from audio and visual data using a multitask learning and a fusion
strategy. First, mult... | electrics |
3,273 | BUT System for the Second DIHARD Speech Diarization Challenge | eess.AS | This paper describes the winning systems developed by the BUT team for the
four tracks of the Second DIHARD Speech Diarization Challenge. For tracks 1 and
2 the systems were mainly based on performing agglomerative hierarchical
clustering (AHC) of x-vectors, followed by another x-vector clustering based on
Bayes hidden... | electrics |
3,274 | Auxiliary Function-Based Algorithm for Blind Extraction of a Moving Speaker | eess.AS | Recently, Constant Separating Vector (CSV) mixing model has been proposed for
the Blind Source Extraction (BSE) of moving sources. In this paper, we
experimentally verify the applicability of CSV in the blind extraction of a
moving speaker and propose a new BSE method derived by modifying the auxiliary
function-based a... | electrics |
3,275 | Vowels and Prosody Contribution in Neural Network Based Voice Conversion Algorithm with Noisy Training Data | eess.AS | This research presents a neural network based voice conversion (VC) model.
While it is a known fact that voiced sounds and prosody are the most important
component of the voice conversion framework, what is not known is their
objective contributions particularly in a noisy and uncontrolled environment.
This model uses ... | electrics |
3,276 | Voice conversion using coefficient mapping and neural network | eess.AS | The research presents a voice conversion model using coefficient mapping and
neural network. Most previous works on parametric speech synthesis did not
account for losses in spectral details causing over smoothing and invariably,
an appreciable deviation of the converted speech from the targeted speaker. An
improved mo... | electrics |
3,277 | Robust Audio Watermarking Using Graph-based Transform and Singular Value Decomposition | eess.AS | Graph-based Transform (GT) has been recently leveraged successfully in the
signal processing domain, specifically for compression purposes. In this paper,
we employ the GBT, as well as the Singular Value Decomposition (SVD) with the
goal to improve the robustness of audio watermarking against different attacks
on the a... | electrics |
3,278 | Acoustic Scene Classification using Audio Tagging | eess.AS | Acoustic scene classification systems using deep neural networks classify
given recordings into pre-defined classes. In this study, we propose a novel
scheme for acoustic scene classification which adopts an audio tagging system
inspired by the human perception mechanism. When humans identify an acoustic
scene, the exi... | electrics |
3,279 | Deep Generative Variational Autoencoding for Replay Spoof Detection in Automatic Speaker Verification | eess.AS | Automatic speaker verification (ASV) systems are highly vulnerable to
presentation attacks, also called spoofing attacks. Replay is among the
simplest attacks to mount - yet difficult to detect reliably. The
generalization failure of spoofing countermeasures (CMs) has driven the
community to study various alternative d... | electrics |
3,280 | Dialect Identification of Spoken North Sámi Language Varieties Using Prosodic Features | eess.AS | This work explores the application of various supervised classification
approaches using prosodic information for the identification of spoken North
S\'ami language varieties. Dialects are language varieties that enclose
characteristics specific for a given region or community. These characteristics
reflect segmental a... | electrics |
3,281 | Low Latency End-to-End Streaming Speech Recognition with a Scout Network | eess.AS | The attention-based Transformer model has achieved promising results for
speech recognition (SR) in the offline mode. However, in the streaming mode,
the Transformer model usually incurs significant latency to maintain its
recognition accuracy when applying a fixed-length look-ahead window in each
encoder layer. In thi... | electrics |
3,282 | Evaluation of Error and Correlation-Based Loss Functions For Multitask Learning Dimensional Speech Emotion Recognition | eess.AS | The choice of a loss function is a critical part of machine learning. This
paper evaluated two different loss functions commonly used in regression-task
dimensional speech emotion recognition, an error-based and a correlation-based
loss functions. We found that using a correlation-based loss function with a
concordance... | electrics |
3,283 | Dual Attention in Time and Frequency Domain for Voice Activity Detection | eess.AS | Voice activity detection (VAD) is a challenging task in low signal-to-noise
ratio (SNR) environment, especially in non-stationary noise. To deal with this
issue, we propose a novel attention module that can be integrated in Long
Short-Term Memory (LSTM). Our proposed attention module refines each LSTM
layer's hidden st... | electrics |
3,284 | Mechanical classification of voice quality | eess.AS | While there is no a priori definition of good singing voices, we tend to make
consistent evaluations of the quality of singing almost instantaneously. Such
an instantaneous evaluation might be based on the sound spectrum that can be
perceived in a short time. Here we devise a Bayesian algorithm that learns to
evaluate ... | electrics |
3,285 | Improved Source Counting and Separation for Monaural Mixture | eess.AS | Single-channel speech separation in time domain and frequency domain has been
widely studied for voice-driven applications over the past few years. Most of
previous works assume known number of speakers in advance, however, which is
not easily accessible through monaural mixture in practice. In this paper, we
propose a... | electrics |
3,286 | On The Differences Between Song and Speech Emotion Recognition: Effect of Feature Sets, Feature Types, and Classifiers | eess.AS | In this paper, we evaluate the different features sets, feature types, and
classifiers on both song and speech emotion recognition. Three feature sets:
GeMAPS, pyAudioAnalysis, and LibROSA; two feature types: low-level descriptors
and high-level statistical functions; and four classifiers: multilayer
perceptron, LSTM, ... | electrics |
3,287 | Subband modeling for spoofing detection in automatic speaker verification | eess.AS | Spectrograms - time-frequency representations of audio signals - have found
widespread use in neural network-based spoofing detection. While deep models
are trained on the fullband spectrum of the signal, we argue that not all
frequency bands are useful for these tasks. In this paper, we systematically
investigate the ... | electrics |
3,288 | Using Cyclic Noise as the Source Signal for Neural Source-Filter-based Speech Waveform Model | eess.AS | Neural source-filter (NSF) waveform models generate speech waveforms by
morphing sine-based source signals through dilated convolution in the time
domain. Although the sine-based source signals help the NSF models to produce
voiced sounds with specified pitch, the sine shape may constrain the generated
waveform when th... | electrics |
3,289 | Deep Multilayer Perceptrons for Dimensional Speech Emotion Recognition | eess.AS | Modern deep learning architectures are ordinarily performed on
high-performance computing facilities due to the large size of the input
features and complexity of its model. This paper proposes traditional
multilayer perceptrons (MLP) with deep layers and small input size to tackle
that computation requirement limitati... | electrics |
3,290 | Emotional Voice Conversion With Cycle-consistent Adversarial Network | eess.AS | Emotional Voice Conversion, or emotional VC, is a technique of converting
speech from one emotion state into another one, keeping the basic linguistic
information and speaker identity. Previous approaches for emotional VC need
parallel data and use dynamic time warping (DTW) method to temporally align the
source-target... | electrics |
3,291 | Multi-Target Emotional Voice Conversion With Neural Vocoders | eess.AS | Emotional voice conversion (EVC) is one way to generate expressive synthetic
speech. Previous approaches mainly focused on modeling one-to-one mapping,
i.e., conversion from one emotional state to another emotional state, with
Mel-cepstral vocoders. In this paper, we investigate building a multi-target
EVC (MTEVC) arch... | electrics |
3,292 | Noise Tokens: Learning Neural Noise Templates for Environment-Aware Speech Enhancement | eess.AS | In recent years, speech enhancement (SE) has achieved impressive progress
with the success of deep neural networks (DNNs). However, the DNN approach
usually fails to generalize well to unseen environmental noise that is not
included in the training. To address this problem, we propose "noise tokens"
(NTs), which are a ... | electrics |
3,293 | An investigation of phone-based subword units for end-to-end speech recognition | eess.AS | Phones and their context-dependent variants have been the standard modeling
units for conventional speech recognition systems, while characters and
subwords have demonstrated their effectiveness for end-to-end recognition
systems. We investigate the use of phone-based subwords, in particular, byte
pair encoder (BPE), a... | electrics |
3,294 | Att-HACK: An Expressive Speech Database with Social Attitudes | eess.AS | This paper presents Att-HACK, the first large database of acted speech with
social attitudes. Available databases of expressive speech are rare and very
often restricted to the primary emotions: anger, joy, sadness, fear. This
greatly limits the scope of the research on expressive speech. Besides, a
fundamental aspect ... | electrics |
3,295 | MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition | eess.AS | We present an MatchboxNet - an end-to-end neural network for speech command
recognition. MatchboxNet is a deep residual network composed from blocks of 1D
time-channel separable convolution, batch-normalization, ReLU and dropout
layers. MatchboxNet reaches state-of-the-art accuracy on the Google Speech
Commands dataset... | electrics |
3,296 | Towards Fast and Accurate Streaming End-to-End ASR | eess.AS | End-to-end (E2E) models fold the acoustic, pronunciation and language models
of a conventional speech recognition model into one neural network with a much
smaller number of parameters than a conventional ASR system, thus making it
suitable for on-device applications. For example, recurrent neural network
transducer (R... | electrics |
3,297 | Can Speaker Augmentation Improve Multi-Speaker End-to-End TTS? | eess.AS | Previous work on speaker adaptation for end-to-end speech synthesis still
falls short in speaker similarity. We investigate an orthogonal approach to the
current speaker adaptation paradigms, speaker augmentation, by creating
artificial speakers and by taking advantage of low-quality data. The base
Tacotron2 model is m... | electrics |
3,298 | Scyclone: High-Quality and Parallel-Data-Free Voice Conversion Using Spectrogram and Cycle-Consistent Adversarial Networks | eess.AS | This paper proposes Scyclone, a high-quality voice conversion (VC) technique
without parallel data training. Scyclone improves speech naturalness and
speaker similarity of the converted speech by introducing CycleGAN-based
spectrogram conversion with a simplified WaveRNN-based vocoder. In Scyclone, a
linear spectrogram... | electrics |
3,299 | Cross-Language Transfer Learning, Continuous Learning, and Domain Adaptation for End-to-End Automatic Speech Recognition | eess.AS | In this paper, we demonstrate the efficacy of transfer learning and
continuous learning for various automatic speech recognition (ASR) tasks. We
start with a pre-trained English ASR model and show that transfer learning can
be effectively and easily performed on: (1) different English accents, (2)
different languages (... | electrics |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.