We investigate how dominant-frequency information can be used in speech feature extraction to increase the robustness of automatic speech recognition against additive background noise. First, we review several earlier proposed auditory-based feature extraction methods and argue that the use of dominant-frequency information might be one of the major reasons for their improved noise robustness. Furthermore, we propose a new feature extraction method, which combines subband power information with dominant subband frequency information in a simple and computationally efficient way. The proposed features are shown to be considerably more robust against additive background noise than standard mel-frequency cepstrum coefficients on two different recognition tasks. The performance improvement increased as we moved from a small-vocabulary isolated-word task to a medium-vocabulary continuous-speech task, where the proposed features also outperformed a computationally expensive auditory-based method. The greatest improvement was obtained for noise types characterized by a relatively flat spectral density.

The paper presents an extensive study of zero crossings with peak amplitudes (ZCPA) features, that have earlier been shown to outperform both conventional and auditory-based features in the presence of additive noise. The study starts by optimizing different parameters involved in ZCPA feature computation, followed by a comparison of ZCPA and MFCC features on two recognition tasks in different background conditions. The main differences between the two feature types are identified, and their individual effects on ASR performance are evaluated. The importance of a proper choice of analysis frame lengths and filter bandwidths in ZCPA feature extraction is demonstrated. Furthermore, the use of dominant frequency information in ZCPA features is found to be a major reason for increased robustness of ZCPA features compared to MFCC features.

In this paper we propose a new framework for utilizing frequency information from the short-term power spectrum of speech. Feature extraction is based on the cepstral coefficients derived from the histograms of subband spectral centroids (SSC). Two new feature extraction algorithms are proposed, one based on frequency information alone, and the other which efficiently combines the frequency and amplitude information from the speech power spectrum. Experimental study on an automatic speech recognition task shows that the proposed methods outperform the conventional speech front-ends in the presence of additive white noise, while they perform comparably in the noise-free conditions.

The paper is concerned with the implementation of automatic speech recognition (ASR) based services on wireless mobile devices. Techniques are investigated for improving the performance of ASR systems in the context of the devices themselves, the environments that they are used in, and the networks they are connected to. A set of ASR tasks and ASR system architectures that are applicable to a wide range of simple mobile devices is presented. A prototype ASR based service is defined and the implementation of the service on a wireless mobile device is described. A database of speech utterances was collected from a population of fifty users interacting with this prototype service in multiple environments. An experimental study was performed where model compensation procedures for improving acoustic robustness and lattice rescoring procedures for reducing task perplexity were evaluated on this speech corpus.

In this paper we propose a new speech parameterization framework that efficiently combines frequency and magnitude information from the short-term power spectrum of speech. This is achieved through computation of subband spectral centroid histograms (SSCH). Relationship between the proposed method and auditory based speech parameterization methods is discussed. An experimental study on an automatic speech recognition task has shown that the proposed method outperforms the conventional speech front-ends in presence of different types of additive noise, while it performs comparably in the noise-free conditions. In the case of car noise, our method also outperforms the computationally expensive auditory based methods, while having simplicity and low computational cost similar to the conventional front-ends.

2. BASELINE SYSTEM We performed simple speech recognition experiments for 4-digit strings to analyze the major errors in spontaneous speech . 2.1 Recognition system •Start-point and end-point detection The input of a realistic recognition system, being a continuous sequence of speech and background events, requires an efficient algorithm to distinguish the speech utterances from the surrounding background. In this study, we implemented a start-point and end-point detector based on HMM likelihoods. For every input speech frame we compared the probability between two HMMs: a background model and a composite all-digit model, whose purpose is to match any digit event. This model was trained using a large database containing samples of all digits. When the difference between the probabilities rises above an experimentally determined threshold, the start-point is set and recognition is started. The comparison is then carried on until the end-point is decided, using the same likelihood-based criterion. The thresholds were first determined so that the largest pause between two digits in a string did not exceed 320ms. •HMMs A feature vector consists of the 16-dimension mean normalized cepstrum vector and 16-dimension delta ABSTRACT In this paper, we describe a new recognition system for 4-digit-strings in Japanese under fluent speech conditions. In particular, we introduce several methods to solve the problems related to the spontaneity of speech: discrimination of speech and background noise, out-ofvocabulary words, pauses between digits, etc. These methods led to an error rate reduction of 76%, compared to a simple startand end-point detection based recognizer using non-refined models.