John Butzberger

Large-vocabulary dictation using SRI's DECIPHER speech recognition system: progressive search techniques

The authors describe a technique called progressive search which is useful for developing and implementing speech recognition systems with high computational requirements. The scheme iteratively uses more and more complex recognition schemes, where each iteration constrains the speech space of the next. An algorithm called the forward-backward word-life algorithm is described. It can generate a word lattice in a progressive search that would be used as a language model embedded in a succeeding recognition pass to reduce computation requirements. It is shown that speed-ups of more than an order of magnitude are achievable with only minor costs in accuracy.<<ETX>>

Spontaneous speech effects in large vocabulary speech recognition applications

We describe three analyses on the effects of spontaneous speech on continuous speech recognition performance. We have found that: (1) spontaneous speech effects significantly degrade recognition performance, (2) fluent spontaneous speech yields word accuracies equivalent to read speech, and (3) using spontaneous speech training data can significantly improve performance for recognizing spontaneous speech. We conclude that word accuracy can be improved by explicitly modeling spontaneous effects in the recognizer, and by using as much spontaneous speech training data as possible. Inclusion of read speech training data, even within the task domain, does not significantly improve performance.

Reduced channel dependence for speech recognition

Speech recognition systems tend to be sensitive to unimportant steady-state variation in speech spectra (i.e. those caused by varying the microphone or channel characteristics). There have been many attempts to solve this problem; however, these techniques are often computationally burdensome, especially for real-time implementation. Recently, Hermansy et al. [1] and Hirsch et al. [2] have suggested a simple technique that removes slow-moving linear channel variation with little adverse effect on speech recognition performance. In this paper we examine this technique, known as RASTA filtering, and evaluate its performance when applied to SRIs DECIPHER™ speech recognition system [3]. We show that RASTA filtering succeeds in reducing DECIPHER™s dependence on the channel.

Techniques to achieve an accurate real-time large-vocabulary speech recognition system

In addressing the problem of achieving high-accuracy real-time speech recognition systems, we focus on recognizing speech from ARPAs 20,000-word Wall Street Journal (WSJ) task, using current UNIX workstations. We have found that our standard approach---using a narrow beam width in a Viterbi search for simple discrete-density hidden Markov models (HMMs)---works in real time with only very low accuracy. Our most accurate algorithms recognize speech many times slower than real time. Our (yet unattained) goal is to recognize speech in real time at or near full accuracy.We describe the speed/accuracy trade-offs associated with several techniques used in a one-pass speech recognition framework:• Trade-offs associated with reducing the acoustic modeling resolution of the HMMs (e.g., output-distribution type, number of parameters, cross-word modeling)• Trade-offs associated with using lexicon trees, and techniques for implementing full and partial bigram grammars with those trees• Computation of Gaussian probabilities are the most time-consuming aspect of our highest accuracy system, and techniques allowing us to reduce the number of Gaussian probabilities computed with little or no impact on speech recognition accuracy.Our results show that tree-based modeling techniques used with appropriate acoustic modeling approaches achieve real-time performance on current UNIX workstations at about a 30% error rate for the WSJ task. The results also show that we can dramatically reduce the computational complexity of our more accurate but slower modeling alternatives so that they are near the speed necessary for real-time performance in a multipass search. Our near-future goal is to combine these two technologies so that real-time, high-accuracy large-vocabulary speech recognition can be achieved.

Progressive-search algorithms for large-vocabulary speech recognition

We describe a technique we call Progressive Search which is useful for developing and implementing speech recognition systems with high computational requirements. The scheme iteratively uses more and more complex recognition schemes, where each iteration constrains the search space of the next. An algorithm, the Forward-Backward Word-Life Algorithm, is described. It can generate a word lattice in a progressive search that would be used as a language model embedded in a succeeding recognition pass to reduce computation requirements. We show that speed-ups of more than an order of magnitude are achievable with only minor costs in accuracy.

DynaSpeak: SRI's scalable speech recognizer for embedded and mobile systems

We introduce SRIs new speech recognition engine, DynaSpeak™, which is characterized by its scalability and flexibility, high recognition accuracy, memory and speed efficiency, adaptation capability, efficient grammar optimization, support for natural language parsing functionality, and operation based on integer arithmetic. These features are designed to address the needs of the fast-developing and changing domain of embedded and mobile computing platforms.