Analysis of Speech Recognition Techniques

Speech recognition has been an intregral part of human life acting as one of the five senses of human body, because of which application developed on the basis of speech recognition has high degree of acceptance. Here in this project we tried to analyse the different steps involved in artificial speech recognition by man-machine interface. The various steps we followed in speech recognition are feature extraction, distance calculation, dynamic time wrapping. We have tried to find out an approach which is both simple and efficient so that it can be utilised in embedded systems. After analysing the steps above we realised the process using small programs using MATLAB which is able to do small no. of isolated word recognition

A Review of Audio Features and Statistical Models Exploited for Voice Pattern Design

Audio fingerprinting, also named as audio hashing, has been well-known as a powerful technique to perform audio identification and synchronization. It basically involves two major steps: fingerprint (voice pattern) design and matching search. While the first step concerns the derivation of a robust and compact audio signature, the second step usually requires knowledge about database and quick-search algorithms. Though this technique offers a wide range of real-world applications, to the best of the authors' knowledge, a comprehensive survey of existing algorithms appeared more than eight years ago. Thus, in this paper, we present a more up-to-date review and, for emphasizing on the audio signal processing aspect, we focus our state-of-the-art survey on the fingerprint design step for which various audio features and their tractable statistical models are discussed.Comment: http://www.iaria.org/conferences2015/PATTERNS15.html ; Seventh International Conferences on Pervasive Patterns and Applications (PATTERNS 2015), Mar 2015, Nice, Franc

Spectro-temporal post-enhancement using MMSE estimation in NMF based single-channel source separation

We propose to use minimum mean squared error (MMSE) estimates to enhance the signals that are separated by nonnegative matrix factorization (NMF). In single channel source separation (SCSS), NMF is used to train a set of basis vectors for each source from their training spectrograms. Then NMF is used to decompose the mixed signal spectrogram as a weighted linear combination of the trained basis vectors from which estimates of each corresponding source can be obtained. In this work, we deal with the spectrogram of each separated signal as a 2D distorted signal that needs to be restored. A multiplicative distortion model is assumed where the logarithm of the true signal distribution is modeled with a Gaussian mixture model (GMM) and the distortion is modeled as having a log-normal distribution. The parameters of the GMM are learned from training data whereas the distortion parameters are learned online from each separated signal. The initial source estimates are improved and replaced with their MMSE estimates under this new probabilistic framework. The experimental results show that using the proposed MMSE estimation technique as a post enhancement after NMF improves the quality of the separated signal

Adaptive Hidden Markov Noise Modelling for Speech Enhancement

A robust and reliable noise estimation algorithm is required in many speech enhancement systems. The aim of this thesis is to propose and evaluate a robust noise estimation algorithm for highly non-stationary noisy environments. In this work, we model the non-stationary noise using a set of discrete states with each state representing a distinct noise power spectrum. In this approach, the state sequence over time is conveniently represented by a Hidden Markov Model (HMM). In this thesis, we first present an online HMM re-estimation framework that models time-varying noise using a Hidden Markov Model and tracks changes in noise characteristics by a sequential model update procedure that tracks the noise characteristics during the absence of speech. In addition the algorithm will when necessary create new model states to represent novel noise spectra and will merge existing states that have similar characteristics. We then extend our work in robust noise estimation during speech activity by incorporating a speech model into our existing noise model. The noise characteristics within each state are updated based on a speech presence probability which is derived from a modified Minima controlled recursive averaging method. We have demonstrated the effectiveness of our noise HMM in tracking both stationary and highly non-stationary noise, and shown that it gives improved performance over other conventional noise estimation methods when it is incorporated into a standard speech enhancement algorithm

Spatial, Spectral, and Perceptual Nonlinear Noise Reduction for Hands-free Microphones in a Car

Speech enhancement in an automobile is a challenging problem because interference can come from engine noise, fans, music, wind, road noise, reverberation, echo, and passengers engaging in other conversations. Hands-free microphones make the situation worse because the strength of the desired speech signal reduces with increased distance between the microphone and talker. Automobile safety is improved when the driver can use a hands-free interface to phones and other devices instead of taking his eyes off the road. The demand for high quality hands-free communication in the automobile requires the introduction of more powerful algorithms. This thesis shows that a unique combination of five algorithms can achieve superior speech enhancement for a hands-free system when compared to beamforming or spectral subtraction alone. Several different designs were analyzed and tested before converging on the configuration that achieved the best results. Beamforming, voice activity detection, spectral subtraction, perceptual nonlinear weighting, and talker isolation via pitch tracking all work together in a complementary iterative manner to create a speech enhancement system capable of significantly enhancing real world speech signals. The following conclusions are supported by the simulation results using data recorded in a car and are in strong agreement with theory. Adaptive beamforming, like the Generalized Side-lobe Canceller (GSC), can be effectively used if the filters only adapt during silent data frames because too much of the desired speech is cancelled otherwise. Spectral subtraction removes stationary noise while perceptual weighting prevents the introduction of offensive audible noise artifacts. Talker isolation via pitch tracking can perform better when used after beamforming and spectral subtraction because of the higher accuracy obtained after initial noise removal. Iterating the algorithm once increases the accuracy of the Voice Activity Detection (VAD), which improves the overall performance of the algorithm. Placing the microphone(s) on the ceiling above the head and slightly forward of the desired talker appears to be the best location in an automobile based on the experiments performed in this thesis. Objective speech quality measures show that the algorithm removes a majority of the stationary noise in a hands-free environment of an automobile with relatively minimal speech distortion

Implementation and evaluation of a low complexity microphone array for speaker recognition

Includes bibliographical references (leaves 83-86).This thesis discusses the application of a microphone array employing a noise canceling beamforming technique for improving the robustness of speaker recognition systems in a diffuse noise field

Centroid-Based Clustering with ab-Divergences

Centroid-based clustering is a widely used technique within unsupervised learning algorithms in many research fields. The success of any centroid-based clustering relies on the choice of the similarity measure under use. In recent years, most studies focused on including several divergence measures in the traditional hard k-means algorithm. In this article, we consider the problem of centroid-based clustering using the family of ab-divergences, which is governed by two parameters, a and b. We propose a new iterative algorithm, ab-k-means, giving closed-form solutions for the computation of the sided centroids. The algorithm can be fine-tuned by means of this pair of values, yielding a wide range of the most frequently used divergences. Moreover, it is guaranteed to converge to local minima for a wide range of values of the pair (a, b). Our theoretical contribution has been validated by several experiments performed with synthetic and real data and exploring the (a, b) plane. The numerical results obtained confirm the quality of the algorithm and its suitability to be used in several practical applications.MINECO TEC2017-82807-

Speaker independent isolated word recognition

The work presented in this thesis concerns the recognition of isolated words using a pattern matching approach. In such a system, an unknown speech utterance, which is to be identified, is transformed into a pattern of characteristic features. These features are then compared with a set of pre-stored reference patterns that were generated from the vocabulary words. The unknown word is identified as that vocabulary word for which the reference pattern gives the best match. One of the major difficul ties in the pattern comparison process is that speech patterns, obtained from the same word, exhibit non-linear temporal fluctuations and thus a high degree of redundancy. The initial part of this thesis considers various dynamic time warping techniques used for normalizing the temporal differences between speech patterns. Redundancy removal methods are also considered, and their effect on the recognition accuracy is assessed. Although the use of dynamic time warping algorithms provide considerable improvement in the accuracy of isolated word recognition schemes, the performance is ultimately limited by their poor ability to discriminate between acoustically similar words. Methods for enhancing the identification rate among acoustically similar words, by using common pattern features for similar sounding regions, are investigated. Pattern matching based, speaker independent systems, can only operate with a high recognition rate, by using multiple reference patterns for each of the words included in the vocabulary. These patterns are obtained from the utterances of a group of speakers. The use of multiple reference patterns, not only leads to a large increase in the memory requirements of the recognizer, but also an increase in the computational load. A recognition system is proposed in this thesis, which overcomes these difficulties by (i) employing vector quantization techniques to reduce the storage of reference patterns, and (ii) eliminating the need for dynamic time warping which reduces the computational complexity of the system. Finally, a method of identifying the acoustic structure of an utterance in terms of voiced, unvoiced, and silence segments by using fuzzy set theory is proposed. The acoustic structure is then employed to enhance the recognition accuracy of a conventional isolated word recognizer