Publications

Compensation of cepstral features for mismatch due to dissimilar train and test conditions has been critical for good performance in many speech applications. Mismatch is typically due to variability from changes in speaker, channel, gender, and environment. Common methods for compensation include RASTA, mean and variance normalization, VTLN, and feature...

In this paper, we introduce a dialect recognition method that makes use of phonetic models adapted per dialect without phonetically labeled data. We show that this method can be implemented efficiently within an existing PRLM system. We compare the performance of this system with other state-of-the-art dialect recognition methods (both...

This paper presents a series of dialect/accent identification results for three sets of dialects with discriminatively trained Gaussian mixture models and feature compensation using eigen-channel decomposition. The classification tasks evaluated in the paper include: 1)the Chinese language classes, 2) American and Indian accented English and 3) discrimination between three Arabic...

This paper presents a description of the MIT Lincoln Laboratory language recognition system submitted to the NIST 2007 Language Recognition Evaluation. This system consists of a fusion of four core recognizers, two based on tokenization and two based on spectral similarity. Results for NIST?s 14-language detection task are presented for...

Discriminative training for language recognition has been a key tool for improving system performance. In addition, recognition directly from shifted-delta cepstral features has proven effective. A recent successful example of this paradigm is SVM-based discrimination of languages based on GMM mean supervectors (GSVs). GSVs are created through MAP adaptation of...

One commonly used approach for language recognition is to convert the input speech into a sequence of tokens such as words or phones and then to use these token sequences to determine the target language. The language classification is typically performed by extracting N-gram statistics from the token sequences and...

In this paper we describe the application of a feature-space transform based on constrained maximum likelihood linear regression for unsupervised compensation of channel and speaker variability to the language recognition problem. We show that use of such transforms can improve baseline GMM-based language recognition performance on the 2005 NIST Language...

High-level characteristics such as word usage, pronunciation, phonotactics, prosody, etc., have seen a resurgence for automatic speaker recognition over the last several years. With the availability of many conversation sides per speaker in current corpora, high-level systems now have the amount of data needed to sufficiently characterize a speaker. Although...

In recent evaluations of automatic language recognition systems, phonotactic approaches have proven highly effective. However, as most of these systems rely on underlying ASR techniques to derive a phonetic tokenization, these techniques are potentially susceptible to acoustic variability from non-language sources (i.e. gender, speaker, channel, etc.). In this paper we...

Automatic language identification is the process by which the language of digitized spoken words is recognized by a computer. It is one of several processes in which information is extracted automatically from a speech signal.