A phone-based approach to non-linguistic speech feature identification

✍ Scribed by Lori F Lamel; Jean-Luc Gauvain

• Publisher: Elsevier Science
• Year: 1995
• Tongue: English
• Weight: 99 KB
• Volume: 9
• Category: Article
• ISSN: 0885-2308
• DOI: 10.1006/csla.1995.0005

✦ Synopsis

In this paper we present a general approach to identifying nonlinguistic speech features from the recorded signal using phone-based acoustic likelihoods. The basic idea is to process the unknown speech signal by feature-specific phone model sets in parallel, and to hypothesize the feature value associated with the model set having the highest likelihood. This technique is shown to be effective for textindependent gender, speaker and language identification. Textindependent speaker identification accuracies of 98•8% on TIMIT (168 speakers) and 99•2% on BREF (65 speakers), were obtained with one utterance per speaker, and 100% with two utterances for both corpora. Experiments in which speaker-specific models were estimated without using the phonetic transcriptions for the TIMIT speakers had the same identification accuracies as those obtained with the use of the transcriptions. French/English language identification is better than 99% with 2 s of read, laboratory speech. For spontaneous telephone speech from the OGI corpus, the language can be identified as French or English with 82% accuracy with 10 s of speech. The ten language identification rate using the OGI corpus was 59•7% with 10 s of signal.