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Mandarin Tone Recognition using Affine-Invariant Prosodic Features and Tone Posteriorgram

Yow-Bang Wang, Lin-Shan Lee INTERSPEECH 2010. Mandarin Tone Recognition using Affine-Invariant Prosodic Features and Tone Posteriorgram. Speaker: Hsiao- Tsung Hung. 1.Introduction. Introduction. Tone recognition are definitely influenced by as least the following: Speaker

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Mandarin Tone Recognition using Affine-Invariant Prosodic Features and Tone Posteriorgram

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  1. Yow-Bang Wang, Lin-Shan Lee INTERSPEECH 2010 Mandarin Tone Recognition using Affine-Invariant Prosodic Features and TonePosteriorgram Speaker:Hsiao-Tsung Hung

  2. 1.Introduction

  3. Introduction • Tone recognition are definitely influenced by as least the following: • Speaker • The “prosodic state” • Co-articulation effect

  4. Introduction • Although the tones depend heavily on many intra-syllabic and prosodic behaviors which are definitely speaker dependent, the native speaker of Mandarin can easily recognize the tones • This implies the tones should be classified by some “robust” prosodic cues, which remain useful across many different conditions.

  5. Introduction • in this paper we try to introduce robustness into prosodic features by different feature normalization schemes, based on the concept of affine invariance property proposed in recent years • We also incorporate the prosodic features with the context information by tone posteriorgram analogous to the TANDEM system for speech recognition.

  6. 2.Proposed Approach

  7. Prosodic feature set

  8. Affine Invariance property • Consider an n-dimensional feature vector sequence along the time axis. If a certain change of condition over these feature vectors is stationary within some period of time, and can be represented as an affine translation:

  9. Affine Invariance property • There may exist some features obtained from which remain invariant under such change of conditions: ,where is the feature function.

  10. Affine invariance for normalized pitch features • Assume the transformation between the pitch contours forthe same syllable for two speakers, and , can beapproximated by an affine transform: (assume here)

  11. Affine invariance for normalized pitch features • relationship between the utterance-level means and standard deviation:

  12. Affine invariance for normalized pitch features • Any feature function M() applied to this normalized pitch contour is automatically affine-invariant.

  13. Invariance of duration and energy features • Duration • Energy • difference for two adjacent syllables

  14. Pitch contour normalization schemes

  15. Tone recognition 21-dimensional prosodic feature vector SVM Enh1 : current syllable Enh2 : current, preceding and following syllable

  16. Experiments

  17. Corpus and experiment setup • Sinica Continuous Speech Prosody Corpora (COSPRO) • Contained 4672 utterances (more than 60,000 syllables), produced by 38 male and 40 female native speakers. • SVM tone recognizers.

  18. Experimental results

  19. Experimental results

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