A Survey on Automatic Speech Recognition with an Illustrative Example on Continuous Speech Recognition of Mandarin

For the past two decades, research in speech recognition has been intensively carried out worldwide, spurred on by advances in signal processing, algorithms, architectures, and hardware. Speech recognition systems have been developed for a wide variety of applications, ranging from small vocabulary keyword recognition over dial-up telephone lines, to medium size vocabulary voice interactive command and control systems on personal computers, to large vocabulary speech dictation, spontaneous speech understanding, and limited-domain speech translation. In this chapter we review some of the key advances in several areas of automatic speech recognition. We also briefly discuss the requirements in designing successful real-world applications and address technical challenges that need to be faced in order to reach the ultimate goal of providing an easy-to-use, natural, and flexible voice interface between people and machines.

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

Combined speech enhancement and auditory modelling for robust distributed speech recognition

Speech recognition can be defined as the process of converting voice signals into the ranks of the word, by applying a specific algorithm that is implemented in a computer program. The research of speech recognition in Indonesia is relatively limited. This paper has studied methods of feature extraction which is the best among the Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficients (MFCC) for speech recognition in Indonesian language. This is important because the method can produce a high accuracy for a particular language does not necessarily produce the same accuracy for other languages, considering every language has different characteristics. Thus this research hopefully can help further accelerate the use of automatic speech recognition for Indonesian language. There are two main processes in speech recognition, feature extraction and recognition. The method used for comparison feature extraction in this study is the LPC and MFCC, while the method of recognition using Hidden Markov Model (HMM). The test results showed that the LPC method is better than MFCC in Indonesian language speech recognition.

In this paper, the improvements in the recently implemented Kannada speech recognition system is demonstrated in detail. The Kannada automatic speech recognition (ASR) system consists of ASR models which are created by using Kaldi, IVRS call flow and weather and agricultural commodity prices information databases. The task specific speech data used in the recently developed spoken dialogue system had high level of different background noises. The different types of noises present in collected speech data had an adverse effect on the on line and off line speech recognition performances. Therefore, to improve the speech recognition accuracy in Kannada ASR system, a noise reduction algorithm is developed which is a fusion of spectral subtraction with voice activity detection (SS-VAD) and minimum mean square error spectrum power estimator based on zero crossing (MMSE-SPZC) estimator. The noise elimination algorithm is added in the system before the feature extraction part. An alternative ASR models are created using subspace Gaussian mixture models (SGMM) and deep neural network (DNN) modeling techniques. The experimental results show that, the fusion of noise elimination technique and SGMM/DNN based modeling gives a better relative improvement of 7.68% accuracy compared to the recently developed GMM-HMM based ASR system. The least word error rate (WER) acoustic models could be used in spoken dialogue system. The developed spoken query system is tested from Karnataka farmers under uncontrolled environment.

Recently with the wide development of computers, various forms of information exchange between human and computer are discovered. At present, interacting with the computer using speech is one of the active scientific research fields especially for people with disabilities who face variety of barriers to computer use. Such research in Automatic Speech Recognition (ASR) is investigated for different languages because each language has its specific features. This paper presents a speech recognition system for individually spoken word in Tamil language using multilayer feed forward network which falls under the category, "Networks for Classification and Prediction" and has widespread interesting applications and functions related to speech processing. To implement the above system, initially preprocessing is done with the input signal and the speech features being the main part of speech recognition system, are analyzed and extracted via Mel Frequency Cepstral Coefficients (MFCC). These feature vectors are given as the input to the Feed-Forward Neural Network for classifying and recognizing Tamil spoken word. Experiments are done with sample Tamil speech signals and its performance are measured based on Mean square error (MSE). The results indicate that the adopted network with specified parameters have produced the best MSE.KeywordsElliptic FilterMel Frequency Cepstral CoefficientsFeed-forward neural networksTamil Speech RecognitionMean square errorPeak signal to Noise Ratio

Large vocabulary speech recognition using subword units

The automatic speech recognition (ASR) system based on deep neural network is easy to be attacked by an adversarial example due to the vulnerability of neural network, which is a hot topic in recent years. The adversarial example does harm to the ASR system, especially if the common-dependent ASR goes wrong, it will lead to serious consequences. To improve the robustness and security of the ASR system, the defense method against adversarial examples must be proposed. Based on this idea, we propose an algorithm of devastation and detection on adversarial examples which can attack the current advanced ASR system. We choose advanced text-dependent and command-dependent ASR system as our target system. Generating adversarial examples by the OPT on text-dependent ASR and the GA-based algorithm on command-dependent ASR. The main idea of our method is input transformation of the adversarial examples. Different random intensities and kinds of noise are added to the adversarial examples to devastate the perturbation previously added to the normal examples. From the experimental results, the method performs well. For the devastation of examples, the original speech similarity before and after adding noise can reach 99.68%, the similarity of the adversarial examples can reach 0%, and the detection rate of the adversarial examples can reach 94%.

Discriminative training techniques have been shown to consistently outperform the maximum likelihood (ML) paradigm for acoustic model training in automatic speech recognition (ASR). Consequently, today's discriminative training methods are fundamental components of state-of-the-art systems and are a major line of research in speech recognition. This article gives a comprehensive overview of discriminative training methods for acoustic model training in the context of ASR. The article covers all related aspects of discriminative training for speech recognition, i.e., specific training criteria and their relation, statistical modeling, different parameter optimization approaches, efficient implementation of discriminative training, and a performance overview.

1. NuSuara Automatic Speech Recognition — an http-based speech recognition engine that leverages on speech interface for transactions such as customer self-service option for: • call centre and help desk • office automation for telephone connection • information inquiry using voice Applications using the convenience of speech command accessed on 31 /08/16 from http://www.nusuara.com/wp-content/uploads/nusuara brochure-6-Apr-12.pdf 2. Universiti Teknologi Malaysia(UTM) Speech recognition research which focuses on developing a system for controlling cars. The aim is to design and develop a system for controlling the windows, radio, horn, etc using Malay as the input voice 3. Universiti Sains Malaysia(USM) Malay-English Code Switching Speech Recognition Project which focuses on the framework, acoustic modelling, pronunciation modelling and language modelling in an automatic speech recognition system. 4. Another project by USM is the development of a spoken document retrieval system which applies automatic speech recognition to allow for more accurate retrieval of information in spoken and multimedia documents.

Voice recognition has become more and more popular in various systems and applications. To further promote Macau tourism worldwide, a mobile Macau tourism APP is being developing that supports voice control to facilitate Portuguese users. Consequently, this paper is about the research and implementation of an Automatic Speech Recognition (ASR) engine for Portuguese language. In this research, three well-known open-source ASR platforms were evaluated and compared. The complete ASR development procedure using Kaldi platform is discussed. Due to the limitation of collected voice data, a novel few-shot learning and transfer learning is implemented in this project. The final model achieved a stable 95.25% accuracy which is good enough for production use. The novel technics implemented in this research can be used for ASR trainings with limited training data and can be extended to a wide range of applications in the future.KeywordsASRPortuguese voice recognitionFew-shot learningTransfer learning

In speech recognition, segmentation involves partitioning a continuous audio signal containing speech into smaller units or segments, such as words, phonemes, or syllables. This process is paramount in speech recognition systems, as it delineates the boundaries between distinct speech elements, facilitating subsequent analysis and processing. Segmentation accuracy significantly impacts speech recognition systems' overall precision and performance, enabling more precise identification and processing of individual speech units. Moreover, proper segmentation empowers the automatic speech recognition (ASR) system to distinguish between different syllables or words effectively, leading to more efficient speech recognition outcomes. This research paper investigates the importance of vowel detection for syllable segmentation in speech recognition, particularly in Arabic speech, such as the Quran, where changes in each syllable can alter the meaning. While existing techniques only consider pronunciation by different readers, this study employs onset detection to account for the presence of Arabic vowels. Specifically, the study focuses on detecting the onset of the recitation of Surah Al-Fatihah's fourth verse using 50 data sets in the syllable detection testing process. The results indicate that syllable detection performs excellently on syllables with /a/ and /i/ vowels. However, syllables with /u/ vowels produce results below 70%. The study suggests that the onset-based method is ideal for syllables with the presence of /a/and /i/ vowels, demonstrating the importance of considering Arabic vowel letters in speech recognition.

The SAPPHIRE system is a powerful, extensible, object oriented toolkit allowing researchers to rapidly build and configure customized speech analysis tools. Implemented in Tcl/Tk and C, the current version of SAPPHIRE provides a wide range of functionality, including the ability to configure and run the SUMMIT speech recognition system. We use SAPPHIRE widely in almost all aspects of our speech analysis and recognition research.

To date, multimodal speech recognition systems based on the processing of audio and video signals show significantly better results than their unimodal counterparts. In general, researchers divide the solution of the audio–visual speech recognition problem into two parts. First, in extracting the most informative features from each modality and second, in the most successful way of fusion both modalities. Ultimately, this leads to an improvement in the accuracy of speech recognition. Almost all modern studies use this approach with video data of a standard recording speed of 25 frames per second. The choice of such a recording speed is easily explained, since the vast majority of existing audio–visual databases are recorded with this rate. However, it should be noticed that the number of 25 frames per second is a world standard for many areas and has never been specifically calculated for speech recognition tasks. The main purpose of this study is to investigate the effect brought by the high-speed video data (up to 200 frames per second) on the speech recognition accuracy. And also to find out whether the use of a high-speed video camera makes the speech recognition systems more robust to acoustical noise. To this end, we recorded a database of audio–visual Russian speech with high-speed video recordings, which consists of records of 20 speakers, each of them pronouncing 200 phrases of continuous Russian speech. Experiments performed on this database showed an improvement in the absolute speech recognition rate up to 3.10%. We also proved that the use of the high-speed camera with 200 fps allows achieving better recognition results under different acoustically noisy conditions (signal-to-noise ratio varied between 40 and 0 dB) with different types of noise (e.g. white noise, babble noise).

The accuracy of automatic speech recognition (ASR) systems is generally evaluated using corpora of grammatically sound read speech or natural spontaneous speech. This prohibits an accurate estimation of the performance of the acoustic modeling part of ASR because the language modeling performance is inherently integrated in the overall performance metric. In this work, ASR and human speech recognition (HSR) accuracies are compared for null grammar sentences in different signal-to-noise ratios and vocabulary sizes-1000, 2000, 4000, and 8000. The results shed light on differences between ASR and HSR in relative significance of bottom-up word recognition and context awareness.