Distributed Speech Recognition

This article discusses possibilities of integrating speech technology into wireless technology, allowing voice input for wireless devices. Distributed speech recognition concept and activities related to its standardization are presented. First ETSI DSR MFCC based standard is described. Work on its extension to improve robustness resulting in new standard is also presented

Band-pass filtering of the time sequences of spectral parameters for robust wireless speech recognition

In this paper we address the problem of automatic speech recognition when wireless speech communication systems are involved. In this context, three main sources of distortion should be considered: acoustic environment, speech coding and transmission errors. Whilst the first one has already received a lot of attention, the last two deserve further investigation in our opinion. We have found out that band-pass filtering of the recognition features improves ASR performance when distortions due to these particular communication systems are present. Furthermore, we have evaluated two alternative configurations at different bit error rates (BER) typical of these channels: band-pass filtering the LP-MFCC parameters or a modification of the RASTA-PLP using a sharper low-pass section perform consistently better than LP-MFCC and RASTA-PLP, respectively.Publicad

Performance Analysis of Advanced Front Ends on the Aurora Large Vocabulary Evaluation

Over the past few years, speech recognition technology performance on tasks ranging from isolated digit recognition to conversational speech has dramatically improved. Performance on limited recognition tasks in noiseree environments is comparable to that achieved by human transcribers. This advancement in automatic speech recognition technology along with an increase in the compute power of mobile devices, standardization of communication protocols, and the explosion in the popularity of the mobile devices, has created an interest in flexible voice interfaces for mobile devices. However, speech recognition performance degrades dramatically in mobile environments which are inherently noisy. In the recent past, a great amount of effort has been spent on the development of front ends based on advanced noise robust approaches. The primary objective of this thesis was to analyze the performance of two advanced front ends, referred to as the QIO and MFA front ends, on a speech recognition task based on the Wall Street Journal database. Though the advanced front ends are shown to achieve a significant improvement over an industry-standard baseline front end, this improvement is not operationally significant. Further, we show that the results of this evaluation were not significantly impacted by suboptimal recognition system parameter settings. Without any front end-specific tuning, the MFA front end outperforms the QIO front end by 9.6% relative. With tuning, the relative performance gap increases to 15.8%. Finally, we also show that mismatched microphone and additive noise evaluation conditions resulted in a significant degradation in performance for both front ends

Exploration and Optimization of Noise Reduction Algorithms for Speech Recognition in Embedded Devices

Environmental noise present in real-life applications substantially degrades the performance of speech recognition systems. An example is an in-car scenario where a speech recognition system has to support the man-machine interface. Several sources of noise coming from the engine, wipers, wheels etc., interact with speech. Special challenge is given in an open window scenario, where noise of traffic, park noise, etc., has to be regarded. The main goal of this thesis is to improve the performance of a speech recognition system based on a state-of-the-art hidden Markov model (HMM) using noise reduction methods. The performance is measured with respect to word error rate and with the method of mutual information. The noise reduction methods are based on weighting rules. Least-squares weighting rules in the frequency domain have been developed to enable a continuous development based on the existing system and also to guarantee its low complexity and footprint for applications in embedded devices. The weighting rule parameters are optimized employing a multidimensional optimization task method of Monte Carlo followed by a compass search method. Root compression and cepstral smoothing methods have also been implemented to boost the recognition performance. The additional complexity and memory requirements of the proposed system are minimum. The performance of the proposed system was compared to the European Telecommunications Standards Institute (ETSI) standardized system. The proposed system outperforms the ETSI system by up to 8.6 % relative increase in word accuracy and achieves up to 35.1 % relative increase in word accuracy compared to the existing baseline system on the ETSI Aurora 3 German task. A relative increase of up to 18 % in word accuracy over the existing baseline system is also obtained from the proposed weighting rules on large vocabulary databases. An entropy-based feature vector analysis method has also been developed to assess the quality of feature vectors. The entropy estimation is based on the histogram approach. The method has the advantage to objectively asses the feature vector quality regardless of the acoustic modeling assumption used in the speech recognition system

Automatic Speech Recognition Using LP-DCTC/DCS Analysis Followed by Morphological Filtering

Front-end feature extraction techniques have long been a critical component in Automatic Speech Recognition (ASR). Nonlinear filtering techniques are becoming increasingly important in this application, and are often better than linear filters at removing noise without distorting speech features. However, design and analysis of nonlinear filters are more difficult than for linear filters. Mathematical morphology, which creates filters based on shape and size characteristics, is a design structure for nonlinear filters. These filters are limited to minimum and maximum operations that introduce a deterministic bias into filtered signals. This work develops filtering structures based on a mathematical morphology that utilizes the bias while emphasizing spectral peaks. The combination of peak emphasis via LP analysis with morphological filtering results in more noise robust speech recognition rates. To help understand the behavior of these pre-processing techniques the deterministic and statistical properties of the morphological filters are compared to the properties of feature extraction techniques that do not employ such algorithms. The robust behavior of these algorithms for automatic speech recognition in the presence of rapidly fluctuating speech signals with additive and convolutional noise is illustrated. Examples of these nonlinear feature extraction techniques are given using the Aurora 2.0 and Aurora 3.0 databases. Features are computed using LP analysis alone to emphasize peaks, morphological filtering alone, or a combination of the two approaches. Although absolute best results are normally obtained using a combination of the two methods, morphological filtering alone is nearly as effective and much more computationally efficient

Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection

Recent advances in Voice Activity Detection (VAD) are driven by artificial and Recurrent Neural Networks (RNNs), however, using a VAD system in battery-operated devices requires further power efficiency. This can be achieved by neuromorphic hardware, which enables Spiking Neural Networks (SNNs) to perform inference at very low energy consumption. Spiking networks are characterized by their ability to process information efficiently, in a sparse cascade of binary events in time called spikes. However, a big performance gap separates artificial from spiking networks, mostly due to a lack of powerful SNN training algorithms. To overcome this problem we exploit an SNN model that can be recast into an RNN-like model and trained with known deep learning techniques. We describe an SNN training procedure that achieves low spiking activity and pruning algorithms to remove 85% of the network connections with no performance loss. The model achieves state-of-the-art performance with a fraction of power consumption comparing to other methods.Comment: 5 pages, 2 figures, 2 table