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Speech Recognition 5 the acoustic data. In essentially all current ASR systems, an intermediate goal for the recognizer is to determine the likelihood of possible speech sound state sequences, where these states typically correspond to a part of a phoneme (although in some small vocabulary systems the states are compon... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 6 | 3 ppur_asr | 0 |
) is the posterior probability of the model given the acoustic input, and the maximum is evaluated over all possible models (i.e., all possible word sequences). Bayes’ rule can be the applied to (1.2), yielding : P(Mi|X) = P(X|Mi, Θ)P(Mi|Θ) P(X|Θ) (1.3) | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 6 | 3 ppur_asr | 0 |
6 ASR statistical formalism where P(X|Mi, Θ) represents the contribution of the acoustic model (i.e., the likelihood that a specific model Mi has produced the acoustic se- quence X), and P(Mi|Θ) represents the contribution of the language model (i.e., the probability of the associated word sequence). For the sake of si... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 7 | 3 ppur_asr | 0 |
∗ A = argmax ΘA J Y j=1 P(Xj|Mj, ΘA) (1.6) = argmax ΘA J X j=1 log P(Xj|Mj, ΘA) (1.7) where (1.6)is referred to as the Maximum Likelihood (ML) criterion, and (1.7) as the log likelihood criterion (1). (1)Although both criteria are equivalent, it is usually more convenient to work with with sum of log likelihoods. | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 7 | 3 ppur_asr | 0 |
Speech Recognition 7 Ideally, the training database and parameter optimization process should target the minimization of the error rate on an independent test set, which is of course not guaranteed by ML training (see further discussion about this in Section 1.3). – Language modeling : The goal of the language model is... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 8 | 3 ppur_asr | 0 |
two-class pro- blem ; it represents 10,000 patterns. On the other hand, the same error percentage on a 100-pattern test set is indistinguishable from chance performance. (2) One way to effectively increase the size of a test set is to (2)For the normal approximation to a binomial distribution, the equivalent standard d... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 8 | 3 ppur_asr | 0 |
8 Pattern classification with realistic data use a “jackknife” procedure, in which each split of the data (e.g., fifths) is used in turn for test after using the remaining part for training. Thus, all of the available data is ultimately used for the test set. Training set size is also a major concern for real problems.... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 9 | 3 ppur_asr | 0 |
criteria would be equivalent. In general, making strong assumptions about the data improves the quality of the estimate – if the assumption is correct. To the extent that these assumptions are wrong, the resulting estimates are poor. Ap- proaches such as EM estimation of Gaussian mixtures and gradient- based neural net... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 9 | 3 ppur_asr | 0 |
Speech Recognition 9 and thus have largely supplanted the earlier, simpler models (e.g., one Gaussian per class). Speech recognition requires the incorporation of “soft” classification decisions as part of the more global decision about the utterance. This requires a dynamic model that represents sequences, rather than... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 10 | 3 ppur_asr | 0 |
ed, SFSA (and HMM in particular) can be used as a production model (where the observation sequence is considered as an output signal being produced by the model) or as a recognition model (acceptor) (where the observation sequence is considered as being ac- cepted by the model). Finally, it may also be the case that th... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 10 | 3 ppur_asr | 0 |
10 Finite State Automata The parameters of these models can be trained by different variants of the powerful Expectation-Maximization (EM) algorithm [1, 15], which, depending on the criterion being used, is referred to as Maximum Li- kelihood (ML) or Maximum A Posteriori (MAP) training. However, al- though being part o... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 11 | 3 ppur_asr | 0 |
of (continuous or discrete) output symbols or vectors. A particular sequence of size N of output symbols/vectors will be denoted Z = ZN 1 = {z1, z2, . . . zn, . . . , zN}, where zn represents the output symbol/vector at time n. – A state transition function sn = f(yn, sn−1), which takes the current input event yn and t... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 11 | 3 ppur_asr | 0 |
Speech Recognition 11 which, depending on the type of automata and optimization criterion, will sometimes be considered as input or output events. To accommo- date this flexibility in the sequel of the present paper, we thus also define the observed sequence of size N as X = XN 1 = {x1, x2, . . . , xn, . . . , xN}, whe... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 12 | 3 ppur_asr | 0 |
In the case of Hidden Markov Models, the emission function is also probabilistic, and the states are no longer directly observable through the input events. Instead, each state produces one of the possible output events with a certain probability. Depending on their structure (as discussed below), transition and emissi... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 12 | 3 ppur_asr | 0 |
12 Finite State Automata Deterministic Stochastic FSA Finite State Automata (SFA) Markov Model HMM HMM/ANN States qk ∈Q xn = qk ∈Q qk ∈Q qk ∈Q Input symbols yn ∈Y — — xn = yn ∈Y Output symbols zn ∈Z — xn = zn ∈Z — Transition law sn = f(yn, sn−1) trans. probabs. trans. probabs. cond. trans. probabs. P(xn|xn−1) P(sn|sn−1... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 13 | 3 ppur_asr | 0 |
Speech Recognition 13 training data. If X represents the whole sequence of training data, and M its associated SFSA, the estimation of optimal parameter set Θ∗is usually achieved by optimizing a Maximum Likelihood (ML) criterion : Θ∗= argmax Θ P(X|M, Θ) (1.8) or a Maximum A Posteriori (MAP) criterion, which could be ei... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 14 | 3 ppur_asr | 0 |
14 Finite State Automata The set of parameters, represented by the (K × K)-transition proba- bility matrix, i.e. Θ = P(xn=ql|xn−1=qk), ∀ql, qk ∈Q is then directly estimated on a large amount of possible observation (and, thus, state) sequences such that : Θ∗= argmax Θ P(X|M, Θ) and simply amounts to estimating the rela... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 15 | 3 ppur_asr | 0 |
Speech Recognition 15 resulting in the standard Viterbi algorithm [26], which can be shown to be a variant of dynamic programming. Although quite simple, Markov models have numerous applications. For example, as already introduced in Section 1.2, they are used in all state-of-the-art continuous speech recognition syste... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 16 | 3 ppur_asr | 0 |
In this case, a HMM is defined as a stochastic finite state automata with a particular (generally strictly left-to-right for speech data) topology. An example of a simple HMM is given in Figure 1.2. In speech recognition, this could be the model of a word or phoneme which is assumed to be composed of three stationary p... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 16 | 3 ppur_asr | 0 |
16 Finite State Automata P(sn = qi|sn−1 = qi) p(xn|sn = qi) p(xn|sn = qj) p(xn|sn = qk) sn = qi sn = qj sn = qk P(sn = qk|sn−1 = qk) P(sn = qj|sn−1 = qj) xn xn xn P(sn = qj|sn−1 = qi) P(sn = qk|sn−1 = qj) Fig. 1.2 A schematic of a three state, left-to-right hidden Markov model (HMM). and decoding is based on the likeli... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 17 | 3 ppur_asr | 0 |
1.8), resulting in the efficient Expectation-Maximization (EM) algorithm [8, 15]. In the rest of this chapter, and to make notation more compact, we will often write the event {sn = qk} (i.e., state qk visited at time n) as qn k. Full likelihood Given this formalism, the likelihood P(X|M) of an observation se- quence X... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 17 | 3 ppur_asr | 0 |
Speech Recognition 17 probabilities. As already done previously, the “full likelihood” P(X|M) could be estimated by summing over all possible paths Q of length N in M, i.e. : P(X|M) = X Q∈M P(X, Q|M) (1.12) which would require roughly 2N ×LN arithmetic operations. (6) However, since events {sn=ql}, i.e. qn l, are mutua... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 18 | 3 ppur_asr | 0 |
Xn−1 1 , M) and can thus be estimated through the forward recurrence : αn(l|M) = X k αn−1(k|M)P(qn l, xn|qn−1 k , Xn−1 1 , M) (1.15) (6)Since there are LN possible state sequences and each state sequence requires ap- proximately 2N calculations. | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 18 | 3 ppur_asr | 0 |
18 Finite State Automata where the sum over k extends over the set of all possible predecessor states qk of ql. Initialization is given by : α1(l|M) = PIl(M) where Π={PIl(M), ∀l= 1, . . . , L} represents the initial state dis- tribution for model M. Assuming a Moore automaton (emission on states), and a first-order Mar... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 19 | 3 ppur_asr | 0 |
.13) and the definition of α, we thus have : P(X|M) = L X l=1 P(qN l, XN 1 |M) = L X l=1 αN(l|M) (1.20) | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 19 | 3 ppur_asr | 0 |
Speech Recognition 19 where the sum usually applies to the states defined as possible final states of M. The full likelihood P(X|M) can thus be estimated as : P(X|M) = L X l=1 N X n=1 αn(l|M)βn(l|M) = X {F} αN(F|M) = X {I} β0(I|M) where {I} and {F} respectively represent the set of possible initial and final states of ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 20 | 3 ppur_asr | 0 |
flows and restates the problem of fin- ding the maximum likelihood path in terms of a standard dynamic pro- gramming recurrence finding the optimal path in model M minimizing the “accumulated distance” D(N, F) by using the following recursion : D(n, l) = d(n, l) + min k [D(n −1, k) + dk,l] (1.23) | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 20 | 3 ppur_asr | 0 |
20 Finite State Automata where D(n, l) = −log P(Xn 1 , qn l), d(n, l) = −log P(xn|qn l), and dk,l= −log P(qn l|qn−1 k ). Running this recursion until until n = N and lreaches the final state F, thus yield −log P(X|M) = D(N, F). During training, the HMM parameters Θ are optimized to maxi- mize the likelihood of a set of... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 21 | 3 ppur_asr | 0 |
of the corresponding target phoneme models, and its parameters are maximized. Over the last few years, a number of la- boratories have demonstrated large-vocabulary (at least 1,000 words), speaker-independent, continuous speech recognition systems based on HMMs. HMMs are models that can deal efficiently with the tempor... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 21 | 3 ppur_asr | 0 |
Speech Recognition 21 However, the assumptions that make the efficiency of these models and their optimization possible limit their generality. As a consequence, they also suffer from several drawbacks including, among others : – Poor discrimination due to the training algorithm which maximizes likelihoods instead of a... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 22 | 3 ppur_asr | 0 |
The- refore a number of hybrid models have been proposed in the literature ; see, e.g., [3]. HMMs are based on a strict probabilistic formalism, making them difficult to interface with other modules in a heterogeneous system. Ho- wever, it has indeed been shown [3, 17] that if each output unit of an (7)Although recurre... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 22 | 3 ppur_asr | 0 |
22 Finite State Automata ANN (typically a multilayer perceptron) is associated with a state qk of the set of states Q = {q1, q2, . . . , qK} on which the SFSA are defined, it is possible to train the ANN (e.g., according to the usual least means square or relative entropy criteria) to generate good estimates of a pos- ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 23 | 3 ppur_asr | 0 |
“Expectation” step. Since this EM version includes an iterative “Maximization” step, it is also sometimes referred to as Ge- neralized EM (GEM). As for standard HMMs, there is a full likelihood version (taking all possible paths into account) as well as a Viterbi ver- sion of the training procedure. Another popular sol... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 23 | 3 ppur_asr | 0 |
Speech Recognition 23 and simply appears as a normalization constant. The scale likelihoods can thus be simply used in a regular Viterbi or forward recurrence to yield an estimator of the global scaled likelihood [9] : P(X|M, Θ) P(X) = X paths N Y n=1 P(xn|sn) P(xn) P(sn|sn−1) (1.26) where the sum extends over all poss... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 24 | 3 ppur_asr | 0 |
the phoneme classes typically are further subdivided into different contextual classes (e.g., with par- ticular neighbors to the left and right), typically resulting in one HMM model (as illustrated in Figure 1.2) per phonetic unit. This is done in | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 24 | 3 ppur_asr | 0 |
24 Acoustic Modeling order to better represent the wide variability in sounds that could be associated with any particular phoneme, for which a primary cause is the coarticulation between neighboring speech sounds due to the inertia in the vocal apparatus. Since using all allowable contexts would lead to a large number... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 25 | 3 ppur_asr | 0 |
Speech Recognition 25 usually modeled by using multi-Gaussian distributions or ANNs, and all the parameters (emission and transition probabilities) of the resulting HMM models are estimated by particular instances of the Expectation- Maximization algorithms. We assume to have access to a large amount of acoustic traini... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 26 | 3 ppur_asr | 0 |
= argmax Qj ∈Mj P(Xj, Qj|Mj, Θ(t) A ) yielding a new segmentation of the training data. This step is often referred to “forced alignment”, since we are forcing the matching of utterances Xj on given models Mj. 3) Given this new segmentation at iteration t, collect all the vectors (over all utterances Xj) associated wit... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 26 | 3 ppur_asr | 0 |
26 Language modeling q q q M j X j Fig. 1.4 Illustration of Viterbi training : starting from a linear segmentation (represented by dashed lines), we esti- mate the best path (by Viterbi dynamic programming recur- sion) matching each utterance Xj on its associated model Mj, yielding a new set of parameters Θ. Until conv... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 27 | 3 ppur_asr | 0 |
Speech Recognition 27 equivalent to the probability of the associated word sequence : P(M) = P(w1, w2, . . . , wl, . . . , wL) (1.29) = L Y l=1 P(wl|w1, w2, . . . , wl−1) (1.30) by simply using the rule of decomposition of joint probabilities. However, estimating all the factors in (1.30) would require too many paramet... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 28 | 3 ppur_asr | 0 |
other. In [11], the concept of equivalence class is also introduced where φ(wl) represents the information required to reliably estimate the conditional probabilities to fulfill the following requirements : – Minimize the number of parameters to estimate – so that available data will be sufficient for the estimation – ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 28 | 3 ppur_asr | 0 |
28 Language modeling which is measured be the language model perplexity, and which is just 2H, where H is the average entropy for a word given the language mo- del (9). In the simple case in which each word has exactly N equally likely successors the perplexity would then be N. In general, adding linguistic predictive ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 29 | 3 ppur_asr | 0 |
frequencies, resulting in the estimate : P(wl|wl−1, wl−2) ≈λ3N(wl|wl−1, wl−2) + λ2N(wl|wl−1) + λ1N(wl) (1.34) with the constraint that P3 k=1 λk = 1. After having estimated the rela- tive frequencies (which are now fixed) on the training data, the optimal values of λk will be estimated on an independent training set (o... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 29 | 3 ppur_asr | 0 |
Speech Recognition 29 referred to as development set) through the Expectation-Maximization algorithm. 1.7 Decoding Recognition of an unknown utterance X is performed by solving (1.5) using local HMM probabilities P(xn|qk), and dynamically integrating language model constraints (probabilities). In principle, this proble... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 30 | 3 ppur_asr | 0 |
, cover all possible sentences. Furthermore, replacing each node of the tree by the associated word HMM (as discus- sed in Section 1.5), we end up with a very large HMM tree in which we have to do the search. (10)In case of higher-order N-gram, we have to take a longer history into account to assign edge transition pro... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 30 | 3 ppur_asr | 0 |
30 Decoding In the case of Viterbi approximation (11), temporal variability is typi- cally handled by dynamic time alignment [4, 21] of the incoming speech representation with the pre-stored models. This time alignment, exploi- ting Bellman’s dynamic programming and optimality principle [2], at- tempts to find the path... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 31 | 3 ppur_asr | 0 |
referred to as beam search (see, e.g., [22]), will be required and only the most promising “tokens” will be expanded (12). – Depth-first : In this case, all the tokens are sorted (in terms of their associated scores) into a “stack”, and we simply keep pursuing only the most promising hypothesis (token with the best sco... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 31 | 3 ppur_asr | 0 |
Speech Recognition 31 of the (unknown) remaining of the path (equivalent to the backward probability in the“forward-backward”algorithm), to guarantee that the different scores in the stack are somehow comparable. If we make sure to underestimate this contribution, the optimality of the search is preserved. However, mor... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 32 | 3 ppur_asr | 0 |
and states, only a limited history need be considered to represent probability of moving from one to the next). In practice, the least viable hypotheses are pruned from the search to save on com- putation, so not all possible hypotheses are actually considered. These ˆosearch errors ̈o (those caused by dropping unlikel... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 32 | 3 ppur_asr | 0 |
32 Bibliography Bibliography [1] Baum, L.E. and Petrie, T.,“Statistical inference for probabilistic func- tions of finite state Markov chains”, Annals of Mathematical Statistics, vol. 37, pp. 1554-1563, 1996. [2] Bellman, R., Dynamic Programming, Princeton University Press, Princeton, 1957. [3] Bourlard, H. and Morgan,... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 33 | 3 ppur_asr | 0 |
hybrid HMM/ANN systems,” Proceedings of Eurospeech’97 (Rhodes, Greece), pp. 1951-1954, 1997. [10] Hopcroft, J., Motwani, R., and Ullman, J., Introduction to Auto- mata Theory, Language and Computations, Second Edition, Addison- Wesley, 2000. [11] Jelinek, F., 1998, Statistical Methods for Speech Recognition, MIT Press,... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 33 | 3 ppur_asr | 0 |
Speech Recognition 33 [14] Konig, Y., Bourlard, H., and Morgan, N., “REMAP : Recursive estimation and maximization of a posteriori probabilities - Appli- cation to transition-based connectionist speech recognition”, in Ad- vances in Neural Information Processing Systems VIII, D.S. Tou- retzky, M.C. Mozer, and M.E. Hass... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 34 | 3 ppur_asr | 0 |
the IEEE, vol. 83, no. 5, pp. 741-770, 1995. [21] Ney, H., “The use of a one-stage dynamic programming algorithm for connected word recognition”, IEEE Trans. on Acoustics, Speech, and Signal Processing, vol. 32, pp. 263-271, 1984 [22] Ney, H. and Aubert, X., “Dynamic programming search : from digit strings to large voc... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 34 | 3 ppur_asr | 0 |
34 Bibliography [25] Robinson, T., Hochberg, M., and Renals, S., “The Use of recurrent neural networks in continuous speech recognition”, Automatic Speech and Speaker Recognition, Kluwer Academic Publishers, pp. 233-258, 1996. [26] Viterbi, A.J., “Error bounds for convolutional codes and an asym- metrically optimum dec... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 35 | 3 ppur_asr | 0 |
Chapter 2 Speaker Verification 2.1 Introduction Speech contains many characteristics that are specific to each indi- vidual, many of which are independent of the linguistic message for an utterance, and which, in speech recognition, are generally considered as a source of degradation. For instance, each utterance from ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 36 | 3 ppur_asr | 0 |
36 Acoustic parameters to databases (e.g., telephone and banking applications), access control to facilities, electronic commerce, information and reservation services, remote access to computer networks, etc. Speaker identification and verification each require the calculation of a score reflecting the distance betwee... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 37 | 3 ppur_asr | 0 |
). The decision also incorporates a threshold that is determined during the enrollment of each new speaker. For a good introduction to speaker recognition, we refer the reader to [4, 5]. 2.2 Acoustic parameters In speech recognition, the main goal of the acoustic processing module is to extract features that are invari... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 37 | 3 ppur_asr | 0 |
Speaker Verification 37 hand, speaker recognition requires the extraction of speaker characteris- tic features, which may be independent of the particular words that were spoken. Such characteristics include the gross properties of the spectral envelope (such as the average formant positions over many vowels) or the av... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 38 | 3 ppur_asr | 0 |
speakers. In general, this sum will be very large, and in the case of speaker verification, should include all possible rival speakers, which is unfortunately impossible. As for speech recognition, parameters for density estimation in spea- ker recognition are determined during a training phase (based on maxi- mum like... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 38 | 3 ppur_asr | 0 |
38 Similarity measures Once these parameters are determined, the denominator in (2.1) is independent of the class and can be neglected for speaker identification. Consequently, and assuming equal prior probabilities for all speakers, a speaker S will be identified as speaker Sc if : P(X|Sc) ≥P(X|Si), ∀i <unk>= c (2.2) ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 39 | 3 ppur_asr | 0 |
hood ratio, we then have : S = Sc if log P(X|Sc) −log P(X|Sc) > ∆c (2.6) where ∆c = log δ ′ c. In general, a large enough value of this difference will mean that the identity of Sc is validated, while it will be rejected in the case of a value below the threshold. Finally, the design of a speaker verification system wi... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 39 | 3 ppur_asr | 0 |
Speaker Verification 39 1) The optimal setting of the decision threshold. As discussed in Sec- tion 2.7, this threshold will usually be estimated by assuming that the distributions of P(X|Sc) and P(X|Sc) are Gaussian. Figure 2.1 represents the typical Gaussian approximation to the distributions of likelihoods P(X|Sc) a... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 40 | 3 ppur_asr | 0 |
, usually called “cohort”, on which P(X|Sc) will be estimated. The cohort is usually defined as the group of speakers whose (2)In addition to normalizing scores, the likelihood ratio will also reduce the effect of some parameters affecting the similarity measure, such as the variability due to differences in transmissi... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 40 | 3 ppur_asr | 0 |
40 Text-dependent speaker verification models are determined to be closed to or more “competitive” with the model of the target speaker Sc [8, 16, 3]. A different cohort is thus assigned to every speaker and is automatically determined during the enrollment phase ; it could also eventually be updated during the en- rol... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 41 | 3 ppur_asr | 0 |
of decision trees can then be used to approximate P(X|Sc). This approach was also shown to be somewhat less sensitive to the optimal setting of the decision threshold. Following [5], we now briefly discuss some of the main speaker verifi- cation approaches. 2.4 Text-dependent speaker verification In text dependent spea... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 41 | 3 ppur_asr | 0 |
Speaker Verification 41 individual, there is a model that encodes both the speaker characteristics as well as the lexical content of the password. In this case, the techniques used for speaker verification are particularly similar to the methods used in speech recognition, namely : 1) Dynamic Time Warping (DTW) approac... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 42 | 3 ppur_asr | 0 |
quence. However, these statistics are a minimal representation of spectral characteristics, and can also be sensitive to the variability of the transfer function of the transmission channel. More recently, an alternative approach has been proposed in which the statistics of dynamic variables (e.g., in the cepstral doma... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 42 | 3 ppur_asr | 0 |
42 Text-independent speaker verification used and modelled by a multi-dimensional autoregressive (AR) mo- del [12]. In [7], different distance measures are compared for this AR approach, and it is shown that performance similar to standard HMM approaches can be achieved. It is also shown that the optimal order of the a... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 43 | 3 ppur_asr | 0 |
; in this approach, distances are stochastic and trained, but otherwise the approach is similar to the determination of codebook entries in vector quantiza- tion. Alternatively, states can be associated with specific classes ; e.g., phones or even coarse phonetic categories. Some temporal constraints will generally be ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 43 | 3 ppur_asr | 0 |
Speaker Verification 43 pically used in speech recognition (e.g., Maximum Mutual Infor- mation [2]) have also been used in speaker verification to improve discrimination between users. – Autoregressive HMMs : In this case, the probability distribution as- sociated with each state is estimated via an autoregressive proc... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 44 | 3 ppur_asr | 0 |
used prior to verification to validate the utterance. Finally, even when the utterance is rejected, the user can still be prompted with an additional sentence. Since the new sentence will be different, the acoustic vector sequence will not be too correlated with the previous one, which will improve the quality of the e... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 44 | 3 ppur_asr | 0 |
44 Identification, verification and the decision threshold repeated sentence would have the same lexical content as the original one. The speech recognition that is used before text-prompted verification is often based on phonetic HMMs, typically using Gaussian or multi- Gaussian distributions. These models are defined... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 45 | 3 ppur_asr | 0 |
, too many cus- tomers will be rejected as impostors ; such an error is referred to as a false rejection. Such a threshold will screen impostors very well, but at the cost of a high customer rejection rate. If the threshold is too low, too many impostors will be accepted as customers ; this kind of error is called a fa... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 45 | 3 ppur_asr | 0 |
Speaker Verification 45 0 0.0005 0.001 0.0015 0.002 0.0025 -2000 -1500 -1000 -500 0 500 1000 1500 2000 2500 train inter train intra test inter test intra threshold Fig. 2.1 Example of Gaussian approximations of the dis- tributions of P(X|Sc) (the left two Gaussians, respectively for training and test set) and P(X|Sc) (... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 46 | 3 ppur_asr | 0 |
46 Bibliography EER threshold is computed on the training set, it will be referred to as a priori threshold, while it will be referred to as a posteriori threshold is computed on the test set. Of course, in real systems, the a posteriori EER measure will not be accessible (since in any one application the system operat... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 47 | 3 ppur_asr | 0 |
- 132, 1998. [7] Griffin, C., Matsui, T., & Furui, S., Distance measures for text- independent speaker recognition based on MAR model, Proc. IEEE Intl. Conf. Acoustics, Speech and Signal Processing (Adelaide, Aus- tralie), pp. I-309-312, 1994. [8] Higgins, A.L., Bahler, L., & Porter, J., Speaker verification using ran-... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 47 | 3 ppur_asr | 0 |
Speaker Verification 47 [10] Matsui, T. & Furui, S., Text-independent speaker recognition using vocal tract and pitch information, Proc. IEEE Intl. Conf. on Acous- tics, Speech, and Signal Processing (Albuquerque, NM), pp. 137-140, 1990. [11] Matsui, T. & Furui, S., Concatenated phoneme models for text- variable speake... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 48 | 3 ppur_asr | 0 |
K., The use of cohort normalized scores for speaker verifica- tion, Proc. Intl. Conf. on Spoken Language Processing, pp. 599-602, 1992. [17] Rosenberg, A.E., Lee, C.-H., & Gokcen, S., Connected word talker verification using whole word hidden Markov models, Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Proces... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 48 | 3 ppur_asr | 0 |
48 Bibliography [20] Tishby, N.Z., On the application of mixture AR hidden Markov mo- dels to text independent speaker recognition, IEEE Trans. Acoustics, Speech, and Signal Processing, vol. ASSP-30, pp. 563-570, 1991. | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/3 ppur_asr.pdf | 49 | 3 ppur_asr | 0 |
Libraries and Mapping Giovanni De Micheli Integrated Systems Laboratory This presentation can be used for non-commercial purposes as long as this note and the copyright footers are not removed © Giovanni De Micheli – All rights reserved | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 0 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 2 Module 1 NObjective LLibraries LProblem formulation and analysis LAlgorithms for library binding based on structural methods | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 1 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 3 Library binding NGiven an unbound logic network and a set of library cells LTransform into an interconnection of instances of library cells LOptimize delay M (under area or power constraints) LOptimize area M Under delay and/or power constraints LOptimize power M Under delay and/or area constr... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 2 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 4 Major approaches NRule-based systems LGeneric, handle all types of cells and situations LHard to obtain circuit with specific properties LData base: M Set of pattern pairs M Local search: detect pattern, implement its best realization NHeuristic algorithms LTypically restricted to single-outpu... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 3 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 5 Library binding: issues NMatching: LA cell matches a sub-network when their terminal behavior is the same LTautology problem LInput-variable assignment problem NCovering: LA cover of an unbound network is a partition into sub-networks which can be replaced by library cells. LBinate covering pr... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 4 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 6 Assumptions NNetwork granularity is fine LDecomposition into base functions: L2-input AND, OR, NAND, NOR NTrivial binding LUse base cells to realize decomposed network LThere exists always a trivial binding: M Base-cost solution... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 5 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 7 Example | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 6 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 8 Example x = b + c y = ax z = xd AND2 4 Cost OR2 Library OA21 4 5 v2 v3 v1 b c d z y x a b c d a x z y m1: {v1,OR2} m2: {v2,AND2} m3: {v3,AND2} m4: {v1,v2,OA21} m5: {v1,v3,OA21} x v3 v2 v1 | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 7 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 9 Example N Vertex covering: L Covering v1 : ( m1 + m4 + m5 ) L Covering v2 : ( m2 + m4 ) L Covering v3 : ( m3 + m5 ) N Input compatibility: L Match m2 requires m1 M (m’2 + m1) L Match m3 requires m1 M (m’3 + m1 ) N Overall binate covering clause L (m1+m4+m5) (m2+m4)(m3+m5)(m’2+m1)(m’3+m1) = 1 x... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 8 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 10 Heuristic approach to library binding NSplit problem into various stages: LDecomposition M Cast network and library in standard form M Decompose into base functions M Example, NAND2 and INV LPartitioning M Break network into cones M Reduce to many multi-input, single-output networks LCovering... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 9 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 11 Decomposition | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 10 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 12 Partitioning | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 11 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 13 Covering | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 12 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 14 Heuristic algorithms NStructural approach LModel functions by patterns M Example: tree, dags LRely on pattern matching techniques NBoolean approach LUse Boolean models LSolve the tautology problem M Use BDD technology LMore powerful | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 13 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 15 Example NBoolean vs. structural matching N f = xy + x’y’ + y’z N g = xy + x’y’ + xz NFunction equality is a tautology L Boolean match NPatterns may be different L Structural match may not exist | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 14 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 17 Example | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 15 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 19 Example SUBJECT TREE PATTERN TREES cost = 2 INV cost = 3 NAND cost = 4 AND cost = 5 OR | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 16 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 20 Example: Lib Match of s: t1 cost = 2 s r u t Match of u: t2 cost = 3 s r u t Match of t: t1 cost = 2+3 = 5 s r u t Match of t: t3 cost = 4 s r u t Match of r: t2 cost = 3+2+4 =9 s r u t Match of r: t4 cost = 5+3 =8 | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 17 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 21 Tree covering NDynamic programming LVisit subject tree bottom up NAt each vertex LAttempt to match: M Locally rooted subtree to all library cell M Find best match and record LThere is always a match when the base cells are in the library NBottom-up search yields and optimum cover NCaveat: LMa... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 18 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 22 Different covering problems NCovering for minimum area: LEach cell has a fixed area cost (label) LArea is additive: M Add area of match to cost of sub-trees NCovering for minimum delay: LDelay is fanout independent M Delay computed with (max, +) rules M Add delay of match to highest cost of s... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 19 | DT15 (lib) | 0 |
(c) Giovanni De Micheli 23 Simple library | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/DT15 (lib).pdf | 20 | DT15 (lib) | 0 |
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