An auditory system-based feature for robust speech recognition
نویسندگان
چکیده
An auditory feature extraction algorithm for robust speech recognition in adverse acoustic environments is presented. The feature computation is comprised of an outer-middle-ear transfer function, FFT, frequency conversion from linear to the Bark scale, auditory filtering, nonlinearity, and discrete cosine transform. The feature is evaluated in two tasks: connected-digit recognition and large vocabulary continuous speech recognition. The tested data were under various noise conditions, including handset and hands-free speech data in landline and wireless communications with additive car and babble noise. Compared with the LPCC, MFCC, MELLPCC, and PLP features, the proposed feature has an average 20% to 30% string error rate reduction on the connected-digit task, and 8% to 14% word error rate reduction on the Wall Street Journal task in various additive noise conditions.
منابع مشابه
Persian Phone Recognition Using Acoustic Landmarks and Neural Network-based variability compensation methods
Speech recognition is a subfield of artificial intelligence that develops technologies to convert speech utterance into transcription. So far, various methods such as hidden Markov models and artificial neural networks have been used to develop speech recognition systems. In most of these systems, the speech signal frames are processed uniformly, while the information is not evenly distributed ...
متن کاملروشی جدید در بازشناسی مقاوم گفتار مبتنی بر دادگان مفقود با استفاده از شبکه عصبی دوسویه
Performance of speech recognition systems is greatly reduced when speech corrupted by noise. One common method for robust speech recognition systems is missing feature methods. In this way, the components in time - frequency representation of signal (Spectrogram) that present low signal to noise ratio (SNR), are tagged as missing and deleted then replaced by remained components and statistical ...
متن کاملA high-performance auditory feature for robust speech recognition
An auditory feature extraction algorithm for robust speech recognition in adverse acoustic environments is proposed. Based on the analysis of human auditory system, the feature extraction algorithm consists of several modules: FFT, outer-middle-ear transfer function, frequency conversion from linear to Bark scales, auditory filtering, nonlinearity, and discrete cosine transform. Three recogniti...
متن کاملAn Information-Theoretic Discussion of Convolutional Bottleneck Features for Robust Speech Recognition
Convolutional Neural Networks (CNNs) have been shown their performance in speech recognition systems for extracting features, and also acoustic modeling. In addition, CNNs have been used for robust speech recognition and competitive results have been reported. Convolutive Bottleneck Network (CBN) is a kind of CNNs which has a bottleneck layer among its fully connected layers. The bottleneck fea...
متن کاملImproving the performance of MFCC for Persian robust speech recognition
The Mel Frequency cepstral coefficients are the most widely used feature in speech recognition but they are very sensitive to noise. In this paper to achieve a satisfactorily performance in Automatic Speech Recognition (ASR) applications we introduce a noise robust new set of MFCC vector estimated through following steps. First, spectral mean normalization is a pre-processing which applies to t...
متن کاملNoise-Robust Speech Recognition Through Auditory Feature Detection and Spike Sequence Decoding
Speech recognition in noisy conditions is a major challenge for computer systems, but the human brain performs it routinely and accurately. Automatic speech recognition (ASR) systems that are inspired by neuroscience can potentially bridge the performance gap between humans and machines. We present a system for noise-robust isolated word recognition that works by decoding sequences of spikes fr...
متن کامل