Publications

This report presents an overview of a program of speech recognition research which was initiated in 1985 with the major goal of developing techniques for robust high performance speech recognition under the stress and noise conditions typical of a military aircraft cockpit. The work on recognition in stress and noise during 1985 and 1986 produced a robust Hidden Markov Model (HMM) isolated-word recognition (IWR) system with 99 percent speaker-dependent accuracy for several difficult stress/noise data bases, and very high performance for normal speech. Robustness techniques which were developed and applied include multi-style training, robust estimation of parameter variances, perceptually-motivated stress-tolerant distance measures, use of time-differential speech parameters, and discriminant analysis. These techniques and others produced more than an order-of-magnitude reduction in isolated-word recognition error rate relative to a baseline HMM system. An important feature of the Lincoln HMM system has been the use of continuous-observation HMM techniques, which provide a good basis for the development of the robustness techniques, and avoid the need for a vector quantizer at the input to the HMM system. Beginning in 1987, the robust HMM system has been extended to continuous speech recognition for both speaker-dependent and speaker-independent tasks. The robust HMM continuous speech recognizer was integrated in real-time with a stressing simulated flight task, which was judged to be very realistic by a number of military pilots. Phrase recognition accuracy on the limited-task-domain (28-word vocabulary) flight task is better than 99.9 percent. Recently, the robust HMM system has been extended to large-vocabulary continuous speech recognition, and has yielded excellent performance in both speaker-dependent and speaker-independent recognition on the DARPA 1000-word vocabulary resource management data base. Current efforts include further improvements to the HMM system, techniques for the integration of speech recognition with natural language processing, and research on integration of neural network techniques with HMM.

A new training procedure called multi-style training has been developed to improve performance when a recognizer is used under stress or in high noise but cannot be trained in these conditions. Instead of speaking normally during training, talkers use different, easily produced, talking styles. This technique was tested using a speech data base that included stress speech produced during a workload task and when intense noise was presented through earphones. A continuous-distribution talker-dependent Hidden Markov Model (HMM) recognizer was trained both normally (5 normally spoken tones) and with multi-style training (one token each from normal, fast, clear, loud, and question-pitch talking styles). The average error rate under stress and normal conditions fell by more than a factor of two with multi-style training and the average error rate under conditions sampled during training fell by a factor of four.

This paper describes a two-stage isolated word search recognition system that uses a Hidden Markov Model (HMM) recognizer in the first stage and a discriminant analysis system in the second stage. During recognition, when the first-stage recognizer is unable to clearly differentiate between acoustically similar words such as "go" and "no" the second-stage discriminator is used. The second-stage system focuses on those parts of the unknown token which are most effective at discriminating the confused words. The system was tested on a 35 word, 10,710 token stress speech isolated word data base created at Lincoln Laboratory. Adding the second-stage discriminating system produced the best results to date on this data base, reducing the overall error rate by more than a factor of two.

Substantial improvements in speech recognition performance on speech produced under stress and in noise have been achieved through the development of techniques for enhancing the robustness of a base-line isolated-word Hidden Markov Model recognizer. The baseline HMM is a continuous-observation system using mel-frequency cepstra as the observation parameters. Enhancement techniques which were developed and tested include: placing a lower limit on the estimated variances of the observations; addition of temporal difference parameters; improved duration modelling; use of fixed diagonal covariance distance functions, with variances adjusted according to perceptual considerations; cepstral domain stress compensation; and multi-style training, where the system is trained on speech spoken with a variety of talking styles. With perceptually-motivated covariance and a combination of normal (single-frame) and differential cepstral observations, average error rates over five simulated-stress conditions were reduced from 20% (baseline) to 2.5% on a simulated-stress data base (105-word vocabulary, eight talkers, five conditions). With variance limiting, normal plus differential observations, and multi-style training, an error rate of 1.8% was achieved. Additional tests were conducted on a data base including nine talkers, eight talking styles, with speech produced under two levels of motor-workload stress. Substantial reductions in error rate were demonstrated for the noise and workload conditions, when multiple talking styles, rather than only normal speech, were used in training. In experiments conducted in simulated fighter cockpit noise, it was shown that error rates could be reduced significantly by training under multiple noise exposure conditions.