Phonetic Analysis and the Automatic Segmentation and Labeling of Speech Sounds (original) (raw)
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This paper presents a novel framework for HMM-based automatic phonetic segmentation that improves the accuracy of placing phone boundaries. In the framework, both training and segmentation approaches are proposed according to the minimum boundary error (MBE) criterion, which tries to minimize the expected boundary errors over a set of possible phonetic alignments. This framework is inspired by the recently proposed minimum phone error (MPE) training approach and the minimum Bayes risk decoding algorithm for automatic speech recognition. To evaluate the proposed MBE framework, we conduct automatic phonetic segmentation experiments on the TIMIT acoustic-phonetic continuous speech corpus. MBE segmentation with MBE-trained models can identify 80.53% of human-labeled phone boundaries within a tolerance of 10 ms, compared to 71.10% identified by conventional ML segmentation with ML-trained models. Moreover, by using the MBE framework, only 7.15% of automatically labeled phone boundaries have errors larger than 20 ms.
There are special topics in cognitive info communication where the processing of continuous speech is necessary. These topics often require the segmentation of speech signal into phoneme sized units. This kind of segmentation is necessary, when the desired behavior depends on speech timing, like rhythm or the place of voiced sounds (emotion or mood detection, language learning, acoustic feature visualization). Segmentation systems based on the acoustic-phonetic knowledge of speech could be realized in a language independent way. In this paper we introduce a language independent solution, based on the segmentation of continuous speech into 9 broad phonetic classes. The classification and segmentation was prepared using Hidden Markov Models. Three databases were used to evaluate the segmentation systems: Hungarian MRBA, German KIEL and English TIMIT databases. 80% average recognition result was obtained.
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Introduction Segmentation is the division of a speech file into non-overlapping sections corresponding to physical or linguistic units. Labelling is the assignment of physical or linguistic labels to these units. Both segmentation and labelling form a major part of current work in linguistic databases. 1.1.1 Segmental transcription The term `transcription' may be used to refer to the representation of a text or an utterance as a string of symbols, without any linkage to the acoustic representation of the utterance. This was the pattern followed by speech and text corpus work during the 1980's, such as the prosodically-transcribed Spoken English Corpus (Knowles et al. 1995). These corpora did not link the symbolic representation with the physical acoustic waveform, and hence were not fully machine-readable. A recent project, MARSEC (Roach et al. 1993), has generated these links for the Spoken English Corpus such that it is now a