Using speaker adaptation parameters, such as maximum likelihood linear regression (MLLR) adaptation matrices, as features for speaker recognition (SR) has been shown to perform well and can also provide complementary information for fusion with other acoustic-based SR systems, such as GMM-based systems. In order to estimate the adaptation parameters, a speech recognizer in the SR domain is required which in turn requires transcribed training data for recognizer training. This limits the approach only to domains where training transcriptions are available. To generalize the adaptation parameter approach to domains without transcriptions, we propose the use of self-organized unit recognizers that can be trained without supervision (or transcribed data). We report results on the 2002 NIST speaker recognition evaluation (SRE2002) extended data set and show that using MLLR parameters estimated from SOU recognizers give comparable performance to systems using a matched recognizers. SOU recognizers also outperform those using cross-lingual recognizers. When we fused the SOU- and word recognizers, SR equal error rate (EER) can be reduced by another 15%. This suggests SOU recognizers can be useful whether or not transcribed data for recognition training are available.

This paper explores both supervised and unsupervised topic modeling for spoken audio documents using only phonetic information. In cases where word-based recognition is unavailable or infeasible, phonetic information can be used to indirectly learn and capture information provided by topically relevant lexical items. In some situations, a lack of transcribed data can prevent supervised training of a same-language phonetic recognition system. In these cases, phonetic recognition can use cross-language models or self-organizing units (SOUs) learned in a completely unsupervised fashion. This paper presents recent improvements in topic modeling using only phonetic information. We present new results using recently developed techniques for discriminative training for topic identification used in conjunction with recent improvements in SOU learning. A preliminary examination of the use of unsupervised latent topic modeling for unsupervised discovery of topics and topically relevant lexical items from phonetic information is also presented.

The availability of real-time continuous speech recognition on mobile and embedded devices has opened up a wide range of research opportunities in human-computer interactive applications. Unfortunately, most of the work in this area to date has been confined to proprietary software, or has focused on limited domains with constrained grammars. In this paper, we present a preliminary case study on the porting and optimization of CMU Sphinx-11, a popular open source large vocabulary continuous speech recognition (LVCSR) system, to hand-held devices. The resulting system operates in an average 0.87 times real-time on a 206 MHz device, 8.03 times faster than the baseline system. To our knowledge, this is the first hand-held LVCSR system available under an open-source license

Presenting complex information in an understandable manner using speech is a challenging task to do well. Significant limitations, both in the generation process and from the human listeners’ capabilities, typically make for poorly understood speech. This work examines possible strategies for producing understandable spoken complex information working within those limitations, as well as identifying ways to improve systems to reduce the limitations’ impact. We discuss a simple user study that explores these strategies with complex structured information, and describe a spoken dialog system that will make use of this work to provide a speech interface to structured information in a more understandable manner. Index Terms: speech synthesis, natural language generation, information presentation.

The Viterbi algorithm has been successfully applied to different pattern recognition and communication tasks. However, if some observations are corrupted by unknown impulsives noise which are not accounted for by the distortion measures, recognition performance can degrade significantly. In this paper, we propose a robust Viterbi algorithm to handle short impulsive noises with unknown characteristics by means of joint decoding and detection during the Viterbi search. To make the algorithm applicable to different noisy conditions with varying amounts of impulsive noise, we further proposed an approach to efficiently estimate the number of corruptions. We demonstrate the effectiveness of the proposed robust algorithms using spoken digit recognition experiments under two different impulsive noise environments. Under random Gaussian replacement noise, the proposed algorithm reduced digit error by more than 65%. Under the GSM network environment in which lost frames are replaced by interpolated neighboring frames, the robust algorithm reduced digit error by 20%. Furthermore, the proposed algorithm does not degrade performance when impulsive noise is not present

Gaussian Mixture Model (GMM) computation is known to be one of the most computation-intensive components in speech decoding. In our previous work, context-independent model based GMM selection (CIGMMS) was found to be an effective way to reduce the cost of GMM computation without significant loss in recognition accuracy. In this work, we propose three methods to further improve the performance of CIGMMS. Each method brings an additional 5-10% relative speed improvement, with a cumulative improvement up to 37% on some tasks. Detailed analysis and experimental results on three corpora are presented.

Computer Science Department

Large vocabulary continuous speech recognition systems are known to be computationally intensive. A major bottleneck is the Gaussian mixture model (GMM) computation and various techniques have been proposed to address this problem. We present a systematic study of fast GMM computation techniques. As there are a large number of these and it is impractical to exhaustively evaluate all of them, we first categorized techniques into four layers and selected representative ones to evaluate in each layer. Based on this framework of study, we provide a detailed analysis and comparison of GMM computation techniques from the four-layer perspective and explore two subtle practical issues, 1) how different techniques can be combined effectively and 2) how beam pruning will affect the performance of GMM computation techniques. All techniques are evaluated in the CMU Communicator domain. We also compare their performance with others reported in the literature. 1.