A probabilistic framework for landmark detection based on phonetic features for automatic speech recognition
| Autor: | Carol Y. Espy-Wilson, Amit Juneja |
|---|---|
| Rok vydání: | 2008 |
| Předmět: |
Consonant
Vocabulary Acoustics and Ultrasonics Computer science Speech recognition media_common.quotation_subject Feature extraction Pronunciation Arts and Humanities (miscellaneous) Phonetics Vowel Feature (machine learning) Hidden Markov model Probability media_common Sonorant Probabilistic logic Signal Processing Computer-Assisted Models Theoretical Markov Chains Word recognition Mel-frequency cepstrum Cues Speech Recognition Software Algorithms |
| Zdroj: | The Journal of the Acoustical Society of America. 123:1154-1168 |
| ISSN: | 0001-4966 |
| DOI: | 10.1121/1.2823754 |
| Popis: | A probabilistic framework for a landmark-based approach to speech recognition is presented for obtaining multiple landmark sequences in continuous speech. The landmark detection module uses as input acoustic parameters (APs) that capture the acoustic correlates of some of the manner-based phonetic features. The landmarks include stop bursts, vowel onsets, syllabic peaks and dips, fricative onsets and offsets, and sonorant consonant onsets and offsets. Binary classifiers of the manner phonetic features-syllabic, sonorant and continuant-are used for probabilistic detection of these landmarks. The probabilistic framework exploits two properties of the acoustic cues of phonetic features-(1) sufficiency of acoustic cues of a phonetic feature for a probabilistic decision on that feature and (2) invariance of the acoustic cues of a phonetic feature with respect to other phonetic features. Probabilistic landmark sequences are constrained using manner class pronunciation models for isolated word recognition with known vocabulary. The performance of the system is compared with (1) the same probabilistic system but with mel-frequency cepstral coefficients (MFCCs), (2) a hidden Markov model (HMM) based system using APs and (3) a HMM based system using MFCCs. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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