This paper presents a statistical technique which can be used to automatically group speech data records based on the similarity of their content. A tree-based clustering algorithm is used to generate a hierarchical structure for the corpus. This structure can then be used to guide the search for similar material in data from other corpora. The SWITCHBOARD Speech Corpus was used to demonstrate these techniques, since it provides sets of speech files which are nominally on the same topic. Excellent automatic clustering was achieved on the truth text transcripts provided with the SWITCHBOARD corpus, with an average cluster purity of 97.3%. Degraded clustering was achieved using the output transcriptions of a speech recognizer, with a clustering purity of 61.4%.

The two largest factors affecting automatic speaker identification performance are the size of the population and the degradations introduced by noisy communication channels (e.g., telephone transmission). To examine experimentally these two factors, this paper presents text-independent speaker identification results for varying speaker population sizes up to 630 speakers for both clean, wideband speech and telephone speech. A system based on Gaussian mixture speaker models is used for speaker identification and experiments are conducted on the TIMIT and NTIMIT databases. This is believed to be the first speaker identification experiments on the complete 630 speaker TIMIT and NTIMIT databases and the largest text-independent speaker identification task reported to date. Identification accuracies of 99.5% and 60.7% are achieved on the TIMIT and NTIMIT databases, respectively. This paper also presents experiments which examine and attempt to quantify the performance loss associated with various telephone degradations by systematically degrading the TIMIT speech in a manner consistent with measured NTIMIT degradations and measuring the performance loss at each step. It is found that the standard degradations of filtering and additive noise do not account for all of the performance gap between the TIMIT and NTIMIT data. Measurements of nonlinear microphone distortions are also described which may explain the additional performance loss.

Investigates a projection-based likelihood measure that significantly improves automatic speech recognition performance in the presence of additive broadband noise. The measure was developed by modifying likelihood scores in continuous Gaussian density hidden Markov models (HMMs), resulting in the weighted projection measure (WPM). Experimental results using the proposed measure are reported for several performance factors: different cepstral-based parameters, normal and multistyle speech, and various noise signals, including white, jittering white, and broadband colored noise. In all cases, significant improvements in speaker-dependent, isolated word recognition were achieved using the WPM instead of the standard Gaussian likelihood measure (weighted Euclidean distance (WED)). As an example, at a SNR of 5 dB, the WPM resulted in improvement in recognition accuracy from 19.4 to 80.6% compared with the standard WED for the DFT mel-cepstral representation.

In this study, a cepstral likelihood measure based on the projection operation is incorporated into a mixture density hidden Markov model (HMM) scheme to improve recognition in the presence of additive noise. The case in which the models are determined only under noise-free conditions is addressed. A background discussion and a derivation of the measure are provided. Recognition experiments are presented showing the usefulness of the proposed measure over the standard Gaussian measure (weighted Euclidean distance) for speaker independent, isolated word recognition in noise. It was found that the proposed mixture weighted projection measure significantly improved performance in several noise types, including white, jittering white, and colored noise. As an example, at an SNR of 10-dB white noise, recognition improved from only 38.4% correct using the Gaussian measure to 83.6% using the developed measure.>

The use of a projection-based cepstral measure for speech recognition in noise is investigated. Interpretations of the measure's spectral and perceptual significance are given, along with its application to cepstral, melcepstral, and delta parameters. It is shown how the projection measure can be incorporated into a continuous density hidden Markov model system in the form of a weighted measure. Both the case where these densities are unimodal Gaussians and mixtures of Gaussians are addressed. In recognition experiments, the weighted projection measure significantly outperformed the standard weighted Euclidean or Gaussian distance measure.>

The directed divergence, which is a measure based on the discrimination information between two signal classes, is investigated. A simplified expression for computing the directed divergence is derived for comparing two Gaussian autoregressive processes such as those found in speech. This expression alleviates both the computational cost (reduced by two thirds) and the numerical problems encountered in computing the directed divergence. In addition, the simplified expression is compared with the Itakura-Saito distance (which asymptotically approaches the directed divergence). Although the expressions for these two distances closely resemble each other, only moderate correlations between the two were found on a set of actual speech data.>