Publications

Phone tokenization followed by n-gram language modeling has consistently provided good results for the task of language identification. In this paper, this technique is generalized by using Gaussian mixture models as the basis for tokenizing. Performance results are presented for a system employing a GMM tokenizer in conjunction with multiple...

The NATO N4 corpus contains speech collected at naval training schools within several NATO countries. The speech utterances comprising the corpus are short, tactical transmissions typical of NATO naval communications. In this paper, we report the results of some preliminary speaker recognition experiments on the N4 corpus. We compare the...

In this paper we examine various ways to derive confidence measures for a language identification system, using phone recognition followed by language models, and describe the application of an evaluation metric for measuring the "goodness" of the different confidence measures. Experiments are conducted on the 1996 NIST Language Identification Evaluation...

Classical speaker and language recognition techniques can be applied to the classification of unknown utterances by computing the likelihoods of the utterances given a set of well trained target models. This paper addresses the problem of grouping unknown utterances when no information is available regarding the speaker or language classes...

This paper studies the structure of foreign-accented read English speech. A system for accent identification is constructed by combining linguistic theory with statistical analysis. Results demonstrate that the linguistic theory is reflected in real speech data and its application improves accent identification. The work discussed here combines and applies previous...

Language-identification (LID) techniques that use multiple single-language phoneme recognizers followed by n-gram language models have consistently yielded top performance at NIST evaluations. In our study of such systems, we have recently cut our LID error rate by modeling the output of n-gram language models more carefully. Additionally, we are now...

A dialect identification technique is described that takes as input extemporaneous, conversational speech spoken in Latin American Spanish and produces as output a hypothesis of the dialect. The system has been trained to recognize Cuban and Peruvian dialects of Spanish, but could be extended easily to other dialects (and languages)...

We have compared the performance of four approaches for automatic language identification of speech utterances: Gaussian mixture model (GMM) classification; single-language phone recognition followed by language-dependent, interpolated n-gram language modeling (PRLM); parallel PRLM, which uses multiple single-language phone recognizers, each trained in a different language; and language dependent parallel phone...

A language identification technique using multiple single-language phoneme recognizers followed by n-gram language models yielded to performance at the March 1994 NIST language identification evaluation. Since the NIST evaluation, work has been aimed at further improving performance by using the acoustic likelihoods emitted from gender-dependent phoneme recognizers to weight the...

This paper compares the performance of four approaches to automatic language identification (LID) of telephone speech messages: Gaussian mixture model classification (GMM), language-independent phoneme recognition followed by language-dependent language modeling (PRLM), parallel PRLM (PRLM-P), and language-dependent parallel phoneme recognition (PPR). These approaches span a wide range of training requirements and...