docs/context/product_spec.md

emotional speech recognition and synthesis - product specification

objective

build a pipeline that ingests human speech, classifies emotion across multiple frameworks, and transcribes text.

inputs and outputs

1. input: audio injection

• source: raw speech material (16khz, mono) recorded via microphone.
• process: audio is streamed to the fine-tuned mistral voxtral endpoint for processing.

2. output: emotional transcription

• content: transcript interleaved with tags from multiple emotional frameworks.
• supported frameworks & reference datasets:
  • russell’s circumplex model: valence and arousal coordinates (Ref: IEMOCAP, EMOVOME).
  • pad emotion space: pleasure, arousal, and dominance dimensions (Ref: IEMOCAP).
  • plutchik’s wheel: categorical tags (Ref: RAVDESS, EmoDB).
  • prosodic analysis: pitch, jitter, and speech rate metadata (Ref: RAVDESS, Berlin Emo-db).
  • stress & deception markers: specialized detection (Ref: SUSAS, MDPE, CRISIS).
• example output: [arousal: high, valence: negative] [stress: probable] [pitch: 210hz] i can't figure this out!

input/output payload definitions

1. speech-to-text request

• endpoint: /v1/recognize
• payload: binary audio/wav
• constraints: max duration 30 seconds.

2. speech-to-text response

• format: json
• schema:

  {
    "text": "i can't figure this out.",
    "emotion": "frustrated",
    "confidence": 0.89
  }
  

error handling

• unrecognized emotion: default to "neutral" tag.
• api timeouts: retry once, then fallback to standard text output.
• audio format errors: reject request with 400 bad request and specify required formatting (16khz, mono).

resources

• front end specification