Speech recognition system using MATLAB : design, implementation, and samples codes

Research in automatic speech recognition has been done for almost four decades. Over the past decades, the development of speech recognition applications gives invaluable contributions. Speech has the potential to be a better interface than other computing devices used such as keyboard or mouse. This project aims to develop automated English digits speech recognition system. The project relies heavily on the well known and widely used statistical method in characterizing the speech pattern, the Hidden Markov Model (HMM), which provides a highly reliable way for recognizing speech. This project discusses the theory of HMM and then extends the ideas to the development and implementation by applying this method in computational speech recognition. Basically, the system is able to recognize the spoken utterances by translating the speech waveform into a set of feature vectors using Mel Frequency Cepstral Coefficients (MFCC) technique, which then estimates the observation likelihood by using the Forward algorithm. The HMM parameters are estimated by applying the Baum Welch algorithm on previously trained samples. The most likely sequence is then decoded using Viterbi algorithm, thus producing the recognized word. This project focuses on all English digits from (Zero through Nine), which is based on isolated words structure. Two modules were developed, namely the isolated words speech recognition and the continuous speech recognition. Both modules were tested in both clean and noisy environments and showed relatively successful recognition rates. In clean environment and isolated words speech recognition module, the multi-speaker mode achieved 99.5% whereas the speaker-independent mode achieved 79.5%. In clean environment and continuous speech recognition module, the multi-speaker mode achieved 70% whereas the speaker-independent mode achieved 55%. However in noisy environment and isolated words speech recognition module, the multi-speaker mode achieved 88% whereas the speaker-independent mode achieved 67%. In noisy environment and continuous speech recognition module, the multi-speaker mode achieved 92.5% whereas the speaker-independent mode achieved 75%. These recognition rates are relatively successful if compared to similar systems

Text-independent speaker recognition

This research presents new text-independent speaker recognition system with multivariate tools such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) embedded into the recognition system after the feature extraction step. The proposed approach evaluates the performance of such a recognition system when trained and used in clean and noisy environments. Additive white Gaussian noise and convolutive noise are added. Experiments were carried out to investigate the robust ability of PCA and ICA using the designed approach. The application of ICA improved the performance of the speaker recognition model when compared to PCA. Experimental results show that use of ICA enabled extraction of higher order statistics thereby capturing speaker dependent statistical cues in a text-independent recognition system. The results show that ICA has a better de-correlation and dimension reduction property than PCA. To simulate a multi environment system, we trained our model such that every time a new speech signal was read, it was contaminated with different types of noises and stored in the database. Results also show that ICA outperforms PCA under adverse environments. This is verified by computing recognition accuracy rates obtained when the designed system was tested for different train and test SNR conditions with additive white Gaussian noise and test delay conditions with echo effect

The Australian English speech corpus for in-car speech processing

The Australian In-Car Speech Corpus is a multi-channel recording of a series of prompts from an in-car navigation task collected over a range of speakers in a variety of driving conditions. Its purpose is to provide a significant resource of speech data appropriate for investigating speech processing needs in the adverse environment of a car. Utterances spoken by 50 speakers were collected in seven different driving conditions, providing the foundation for investigation into noisy, speaker-independent speech processing. Speech recognition experiments are performed to validate the data, to provide baseline results for in-car speech recognition research, and to show that this data can improve speech recognition performance under adverse in-car conditions for Australian English when adapting from American English acoustic models

Towards glottal source controllability in expressive speech synthesis

In order to obtain more human like sounding humanmachine interfaces we must first be able to give them expressive capabilities in the way of emotional and stylistic features so as to closely adequate them to the intended task. If we want to replicate those features it is not enough to merely replicate the prosodic information of fundamental frequency and speaking rhythm. The proposed additional layer is the modification of the glottal model, for which we make use of the GlottHMM parameters. This paper analyzes the viability of such an approach by verifying that the expressive nuances are captured by the aforementioned features, obtaining 95% recognition rates on styled speaking and 82% on emotional speech. Then we evaluate the effect of speaker bias and recording environment on the source modeling in order to quantify possible problems when analyzing multi-speaker databases. Finally we propose a speaking styles separation for Spanish based on prosodic features and check its perceptual significance

Binaural scene analysis : localization, detection and recognition of speakers in complex acoustic scenes

The human auditory system has the striking ability to robustly localize and recognize a specific target source in complex acoustic environments while ignoring interfering sources. Surprisingly, this remarkable capability, which is referred to as auditory scene analysis, is achieved by only analyzing the waveforms reaching the two ears. Computers, however, are presently not able to compete with the performance achieved by the human auditory system, even in the restricted paradigm of confronting a computer algorithm based on binaural signals with a highly constrained version of auditory scene analysis, such as localizing a sound source in a reverberant environment or recognizing a speaker in the presence of interfering noise. In particular, the problem of focusing on an individual speech source in the presence of competing speakers, termed the cocktail party problem, has been proven to be extremely challenging for computer algorithms. The primary objective of this thesis is the development of a binaural scene analyzer that is able to jointly localize, detect and recognize multiple speech sources in the presence of reverberation and interfering noise. The processing of the proposed system is divided into three main stages: localization stage, detection of speech sources, and recognition of speaker identities. The only information that is assumed to be known a priori is the number of target speech sources that are present in the acoustic mixture. Furthermore, the aim of this work is to reduce the performance gap between humans and machines by improving the performance of the individual building blocks of the binaural scene analyzer. First, a binaural front-end inspired by auditory processing is designed to robustly determine the azimuth of multiple, simultaneously active sound sources in the presence of reverberation. The localization model builds on the supervised learning of azimuthdependent binaural cues, namely interaural time and level differences. Multi-conditional training is performed to incorporate the uncertainty of these binaural cues resulting from reverberation and the presence of competing sound sources. Second, a speech detection module that exploits the distinct spectral characteristics of speech and noise signals is developed to automatically select azimuthal positions that are likely to correspond to speech sources. Due to the established link between the localization stage and the recognition stage, which is realized by the speech detection module, the proposed binaural scene analyzer is able to selectively focus on a predefined number of speech sources that are positioned at unknown spatial locations, while ignoring interfering noise sources emerging from other spatial directions. Third, the speaker identities of all detected speech sources are recognized in the final stage of the model. To reduce the impact of environmental noise on the speaker recognition performance, a missing data classifier is combined with the adaptation of speaker models using a universal background model. This combination is particularly beneficial in nonstationary background noise

Deep Learning Based Speech Enhancement and Its Application to Speech Recognition

Speech enhancement is the task that aims to improve the quality and the intelligibility of a speech signal that is degraded by ambient noise and room reverberation. Speech enhancement algorithms are used extensively in many audio- and communication systems, including mobile handsets, speech recognition, speaker verification systems and hearing aids. Recently, deep learning has achieved great success in many applications, such as computer vision, nature language processing and speech recognition. Speech enhancement methods have been introduced that use deep-learning techniques, as these techniques are capable of learning complex hierarchical functions using large-scale training data. This dissertation investigates the deep learning based speech enhancement and its application to robust Automatic Speech Recognition (ASR). We start our work by exploring generative adversarial network (GAN) based speech enhancement. We explore the techniques to extract information about the noise to aid in the reconstruction of the speech signals. The proposed framework, referred to as ForkGAN, is a novel general adversarial learning-based framework that combines deep-learning with conventional noise reduction techniques. We further extend ForkGAN to M-ForkGAN, which integrates feature mapping and mask learning into a unified framework using ForkGAN. Another variant of ForkGAN, named S-ForkGAN, operates on spectral-domain features, which could directly apply to ASR. Systematic evaluations demonstrate the effectiveness of the proposed approaches. Then, we propose a novel multi-stage learning speech enhancement system. Each stage comprises a self-attention (SA) block followed by stacks of temporal convolutional network (TCN) blocks with doubling dilation factors. Each stage generates a prediction that is refined in a subsequent stage. A fusion block is inserted at the input of later stages to re-inject original information. Moreover, we design several multi-scale architectures with perceptual loss. Experiments show that our proposed architectures can achieve the state of the art performance on several public datasets. Recently, modeling to learn the acoustic noisy-clean speech mapping has been enhanced by including auxiliary information such as visual cues, phonetic and linguistic information, and speaker information. We propose a novel speaker-aware speech enhancement (SASE) method that extracts speaker information from a clean reference using long short-term memory (LSTM) layers, and then uses a convolutional recurrent neural network (CRN) to embed the extracted speaker information. The SASE framework is extended with a self-attention mechanism. It is shown that a few seconds of clean reference speech is sufficient, and that the proposed SASE method performs well for a wide range of scenarios. Even though speech enhancement methods that are based on deep learning have demonstrated state-of-the-art performance when compared with conventional methodologies, current deep learning approaches heavily rely on supervised learning, which requires a large number of noisy- and clean-speech sample pairs for training. This is generally not practical in a realistic environment. One cannot simultaneously obtain both noisy and clean speech samples. Thus, most speech enhancement approaches are trained with simulated speech and clean targets. In addition, it would be hard to collect large-scale dataset for the low-resource languages. We propose a novel noise-to-noise speech enhancement (N2N-SE) method that addresses the parallel noisy-clean training data issue, we leverage signal reconstruction techniques by only using corrupted speech. The proposed N2N-SE framework includes a noise conversion module that is an auto-encoder that learns to mix noise with speech, and a speech enhancement module, that learns to reconstruct corrupted speech signals. In addition to additive noise, speech is also affected by reverberation, which is caused by the attenuated and delayed reflections of sound waves. These distortions, particularly when combined, can severely degrade speech intelligibility for human listeners and impact applications, e.g., automatic speech recognition (ASR) and speaker recognition. Thus, effective speech denoising and dereverberation will benefit both speech processing applications and human listeners. We investigate the deep-learning based approaches for both speech dereverberation and speech denoising using the cascade Conformer architecture. The experimental results show that the proposed cascade Conformer can be effective to suppress the noise and reverberation