Differential encoding techniques applied to speech signals

The increasing use of digital communication systems has produced a continuous search for efficient methods of speech encoding. This thesis describes investigations of novel differential encoding systems. Initially Linear First Order DPCM systems employing a simple delayed encoding algorithm are examined. The systems detect an overload condition in the encoder, and through a simple algorithm reduce the overload noise at the expense of some increase in the quantization (granular) noise. The signal-to-noise ratio (snr) performance of such d codec has 1 to 2 dB's advantage compared to the First Order Linear DPCM system. In order to obtain a large improvement in snr the high correlation between successive pitch periods as well as the correlation between successive samples in the voiced speech waveform is exploited. A system called "Pitch Synchronous First Order DPCM" (PSFOD) has been developed. Here the difference Sequence formed between the samples of the input sequence in the current pitch period and the samples of the stored decoded sequence from the previous pitch period are encoded. This difference sequence has a smaller dynamic range than the original input speech sequence enabling a quantizer with better resolution to be used for the same transmission bit rate. The snr is increased by 6 dB compared with the peak snr of a First Order DPCM codea. A development of the PSFOD system called a Pitch Synchronous Differential Predictive Encoding system (PSDPE) is next investigated. The principle of its operation is to predict the next sample in the voiced-speech waveform, and form the prediction error which is then subtracted from the corresponding decoded prediction error in the previous pitch period. The difference is then encoded and transmitted. The improvement in snr is approximately 8 dB compared to an ADPCM codea, when the PSDPE system uses an adaptive PCM encoder. The snr of the system increases further when the efficiency of the predictors used improve. However, the performance of a predictor in any differential system is closely related to the quantizer used. The better the quantization the more information is available to the predictor and the better the prediction of the incoming speech samples. This leads automatically to the investigation in techniques of efficient quantization. A novel adaptive quantization technique called Dynamic Ratio quantizer (DRQ) is then considered and its theory presented. The quantizer uses an adaptive non-linear element which transforms the input samples of any amplitude to samples within a defined amplitude range. A fixed uniform quantizer quantizes the transformed signal. The snr for this quantizer is almost constant over a range of input power limited in practice by the dynamia range of the adaptive non-linear element, and it is 2 to 3 dB's better than the snr of a One Word Memory adaptive quantizer. Digital computer simulation techniques have been used widely in the above investigations and provide the necessary experimental flexibility. Their use is described in the text

Multi-task deep neural network acoustic models with model adaptation using discriminative speaker identity for whisper recognition

This paper presents a study on large vocabulary continuous whisper automatic recognition (wLVCSR). wLVCSR provides the ability to use ASR equipment in public places without concern for disturbing others or leaking private information. However the task of wLVCSR is much more challenging than normal LVCSR due to the absence of pitch which not only causes the signal to noise ratio (SNR) of whispers to be much lower than normal speech but also leads to flatness and formant shifts in whisper spectra. Furthermore, the amount of whisper data available for training is much less than for normal speech. In this paper, multi-task deep neural network (DNN) acoustic models are deployed to solve these problems. Moreover, model adaptation is performed on the multi-task DNN to normalize speaker and environmental variability in whispers based on discriminative speaker identity information. On a Mandarin whisper dictation task, with 55 hours of whisper data, the proposed SI multi-task DNN model can achieve 56.7% character error rate (CER) improvement over a baseline Gaussian Mixture Model (GMM), discriminatively trained only using the whisper data. Besides, the CER of the proposed model for normal speech can reach 15.2%, which is close to the performance of a state-of-the-art DNN trained with one thousand hours of speech data. From this baseline, the model-adapted DNN gains a further 10.9% CER reduction over the generic model

Purging of silence for robust speaker identification in colossal database

The aim of this work is to develop an effective speaker recognition system under noisy environments for large data sets. The important phases involved in typical identification systems are feature extraction, training and testing. During the feature extraction phase, the speaker-specific information is processed based on the characteristics of the voice signal. Effective methods have been proposed for the silence removal in order to achieve accurate recognition under noisy environments in this work. Pitch and Pitch-strength parameters are extracted as distinct features from the input speech spectrum. Multi-linear principle component analysis (MPCA) is is utilized to minimize the complexity of the parameter matrix. Silence removal using zero crossing rate (ZCR) and endpoint detection algorithm (EDA) methods are applied on the source utterance during the feature extraction phase. These features are useful in later classification phase, where the identification is made on the basis of support vector machine (SVM) algorithms. Forward loking schostic (FOLOS) is the efficient large-scale SVM algorithm that has been employed for the effective classification among speakers. The evaluation findings indicate that the methods suggested increase the performance for large amounts of data in noise ecosystems

BaNa: a noise resilient fundamental frequency detection algorithm for speech and music

Fundamental frequency (F0) is one of the essential features in many acoustic related applications. Although numerous F0 detection algorithms have been developed, the detection accuracy in noisy environments still needs improvement. We present a hybrid noise resilient F0 detection algorithm named BaNa that combines the approaches of harmonic ratios and Cepstrum analysis. A Viterbi algorithm with a cost function is used to identify the F0 value among several F0 candidates. Speech and music databases with eight different types of additive noise are used to evaluate the performance of the BaNa algorithm and several classic and state-of-the-art F0 detection algorithms. Results show that for almost all types of noise and signal-to-noise ratio (SNR) values investigated, BaNa achieves the lowest Gross Pitch Error (GPE) rate among all the algorithms. Moreover, for the 0 dB SNR scenarios, the BaNa algorithm is shown to achieve 20% to 35% GPE rate for speech and 12% to 39% GPE rate for music. We also describe implementation issues that must be addressed to run the BaNa algorithm as a real-time application on a smartphone platform.Peer ReviewedPostprint (author's final draft

Time-scale and pitch modifications of speech signals and resynthesis from the discrete short-time Fourier transform

The modification methods described in this paper combine characteristics of PSOLA-based methods and algorithms that resynthesize speech from its short-time Fourier magnitude only. The starting point is a short-time Fourier representation of the signal. In the case of duration modification, portions, in voiced speech corresponding to pitch periods, are removed from or inserted in this representation. In the case of pitch modification, pitch periods are shortened or extended in this representation, and a number of pitch periods is inserted or removed, respectively. Since it is an important tool for both duration and pitch modification, the resynthesis-from-short-time-Fourier-magnitude-only method of Griffin and Lim (1984) and Griffin et al. (1984) is reviewed and adapted. Duration and pitch modification methods and their results are presented.\ud \u

A Novel Method For Speech Segmentation Based On Speakers' Characteristics

Speech Segmentation is the process change point detection for partitioning an input audio stream into regions each of which corresponds to only one audio source or one speaker. One application of this system is in Speaker Diarization systems. There are several methods for speaker segmentation; however, most of the Speaker Diarization Systems use BIC-based Segmentation methods. The main goal of this paper is to propose a new method for speaker segmentation with higher speed than the current methods - e.g. BIC - and acceptable accuracy. Our proposed method is based on the pitch frequency of the speech. The accuracy of this method is similar to the accuracy of common speaker segmentation methods. However, its computation cost is much less than theirs. We show that our method is about 2.4 times faster than the BIC-based method, while the average accuracy of pitch-based method is slightly higher than that of the BIC-based method.Comment: 14 pages, 8 figure