Turkish Speech Recognition Based On Deep Neural Networks

The aim of this paper is to exhibit a comparative case study of the conventional speech recognition GMM-HMM (Gaussian mixture model - hidden Markov model) architecture and the recent model based on deep neural networks. During years the GMM approach has controlled the speech recognition tasks, however it has been surpassed with the resurgence of artificial neural networks. To exemplify these acoustic modeling frameworks, a case study has been conducted by using the Kaldi toolkit, employing a personalized speaker-independent mid-vocabulary voice corpus for recognition of digit strings and personal name lists in latin spanish on a connected-words phone dialing task. The speech recognition accuracy obtained in the results shows a better word error rate by using the DNN acoustic modeling. A 20.71% relative improvement is obtained with DNN-HMM models (3.33% WER) in respect to the lowest GMM-HMM rate (4.20% WER).

This paper reports the development of a multi-genre Urdu Broadcast (BC) corpus and a Large Vocabulary Continuous Speech Recognition (LVCSR) system. BC speech corpus of 98 hours from 453 speakers is collected and annotated. For acoustic modeling, Time-delay Neural Network (TDNN) is developed with prior Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) training and alignments. For the language model, 3-gram, 4-gram and Recurrent Neural Network (RNN) based models are developed on a text corpus of 188 million words. The developed models are tested on 4.3 hours of unseen BC multi-genre speech dataset and the best Word Error Rate (WER) 18.59% is achieved using RNN based Language Model (LM). Moreover, a detailed word error analysis is carried out to compare the errors made by humans and the Automatic Speech Recognition (ASR) System. The results showed a similar behavior of word misrecognitions by both humans and ASR.

The histogram equalization approach is an efficient feature normalization technique for noise robust automatic speech recognition. However, it suffers from performance degradation when some fundamental conditions are not satisfied in the test environment. To remedy these limitations of the original histogram equalization methods, class-based histogram equalization approach has been proposed. Although this approach showed substantial performance improvement under noise environments, it still suffers from performance degradation due to the overfitting problem when test data are insufficient. To address this issue, the proposed histogram equalization technique employs the Bayesian estimation method in the test cumulative distribution function estimation. It was reported in a previous study conducted on the Aurora-4 task that the proposed approach provided substantial performance gains in speech recognition systems based on the acoustic modeling of the Gaussian mixture model-hidden Markov model. In this work, the proposed approach was examined in speech recognition systems with deep neural network-hidden Markov model (DNN-HMM), the current mainstream speech recognition approach where it also showed meaningful performance improvement over the conventional maximum likelihood estimation-based method. The fusion of the proposed features with the mel-frequency cepstral coefficients provided additional performance gains in DNN-HMM systems, which otherwise suffer from performance degradation in the clean test condition.

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%.

This paper presents a system for improving the quality of pronunciation error detection and correction for Qur’an recitation by Non-Arabic speakers. Most of the classical speech recognition systems are built using the Hidden Markov Model (HMM) with a Mixture of Gaussian Model (GMM). This paper attempts to enhance the GMM-HMM model’s performance by using Deep Neural Networks (DNNs). The major part of the work done in this paper is involved in the collection and processing of speakers’ data, and building and evaluation of baseline GMM system and the proposed DNN acoustic models for the Qur’an recitation framework. With the aim of solving some pronunciation problems and enhancing the overall performance of such a speech recognition system, we replace the mixture of Gaussians with a DNN. The DNN-HMM model outperforms the GMM-HMM model by 1.02% based on HTK’s word accuracy equation. By calculating the insertion results for both models, DNN-HMM showed progress by 2.59%. In addition, in substitution results, DNN-HMM shows progress with the confusion phonemes DAA by 15.09% and DHA by 17.28%. All experiments and results are presented and discussed in detail.

Automatic lip-reading (ALR) is a challenging task and a significant amount of research has been devoted to this topic in recent years. However, continuous Russian speech recognition still remains a not well-investigated area. In this paper, we present the results of Russian visual speech recognition (VSR) system using pixel-based and advanced geometry-based features. A HAVRUS video database, comprising of 4000 utterances of continuous Russian speech, collected from 20 speakers, is used in this study. Pixel-based features (principal component analysis-based or PCA) and geometry-based features (active appearance model-based or AAM) were used for the feature representation, and a Gaussian mixture hidden Markov models (HMM) were used for classification. Our evaluation experiments show a significant improvement (up to 9%) in recognition accuracy by using proposed geometry-based features when compared to pixel-based PCA features. The combined VSR is planned for future studies to augment the performance of audio-based automatic speech recognition systems in human–robot interfaces.

This paper presents a deep neural network (DNN) based large vocabulary continuous speech recognition (LVCSR) system for Serbian, developed using the open-source Kaldi speech recognition toolkit. The DNNs are initialized using stacked restricted Boltzmann machines (RBMs) and trained using cross-entropy as the objective function and the standard error backpropagation procedure in order to provide posterior probability estimates for the hidden Markov model (HMM) states. Emission densities of HMM states are represented as Gaussian mixture models (GMMs). The recipes were modified based on the particularities of the Serbian language in order to achieve the optimal results. A corpus of approximately 90 hours of speech (21000 utterances) is used for the training. The performances are compared for two different sets of utterances between the baseline GMM-HMM algorithm and various DNN settings.KeywordsKaldi speech recognition toolkitContinuous speech recognitionDeep neural networksSerbian

In this paper, we propose a novel boosted mixture learning (BML) framework for Gaussian mixture HMMs in speech recognition. BML is an incremental method to learn mixture models for classification problem. In each step of BML, one new mixture component is calculated according to functional gradient of an objective function to ensure that it is added along the direction to maximize the objective function the most. Several techniques have been proposed to extend BML from simple mixture models like Gaussian mixture model (GMM) to Gaussian mixture hidden Markov model (HMM), including Viterbi approximation to obtain state segmentation, weight decay to initialize sample weights to avoid overfitting, combining partial updating with global updating of parameters and using Bayesian information criterion (BIC) for parsimonious modeling. Experimental results on the WSJ0 task have shown that the proposed BML yields relative word and sentence error rate reduction of 10.9% and 12.9%, respectively, over the conventional training procedure.

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.

Combined speech enhancement and auditory modelling for robust distributed speech recognition

In recent years, with the rapid development of Deep Learning (DL) and the widespread uses of Deep Neural Networks (DNN), speech recognition technology has attracted great attention. This technology enables machines to convert human speech into text and plays an important role in some areas such as virtual assistants, conference transcription and smart home devices. Traditional models such as Hidden Markov Models (HMM) and Gaussian Mixture Models (GMM) used to play a key role in modeling and recognizing speech signals. Even though these methods still have challenges in dealing with high‐dimensional feature vectors as they struggle to efficiently learn large amounts of model parameters while maintaining good generalization performance. The emergence of DNNs has revolutionized the field of speech recognition, making it possible to automatically extract features from raw speech signals. Thereby, modern speech recognition systems can exhibit higher adaptability and generalization capabilities, achieving higher accuracy and stability under various conditions. This review explores the application of DL techniques to speech recognition, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformer‐based architectures, etc. Advances in these techniques have dramatically improved recognition accuracy, noise immunity, and adaptability to multiple accents, thereby improving the total efficiency of automatic speech systems. By leveraging advanced feature extraction and modeling capabilities, deep learning‐based systems support real‐ time processing and minimize the dependency on traditional manually designed features. However, there are still limitations in terms of interpretability, computational efficiency, and fairness to different accents and languages, which impede the widespread use of these techniques. Future research should focus on solving these issues in order to achieve fairer, efficient, and scalable deep learning‐driven speech recognition schemes.

It is of great practical significance to study intelligent robots for crack detection of urban underground pipes instead of manual detection, which not only can save time and cost, but also can improve the accuracy rate of identification. The system consists of two parts: mobile platform and system detection platform. The mobile platform is obstacle-avoiding by ultrasonic module, segmenting the pipeline color by camera and issuing platform steering command. The system detection platform adopts Raspberry Pi as the core control system, and collects the pipeline leakage audio through the pickups, and the collected audio is passed through Gaussian mixture model-Hidden Markov model (GMM -HMM) and Deep neural networks- Hidden Markov model (DNN -HMM) respectively in Raspberry Pi model (DNN -HMM) for the purpose of detection. The accuracy and running speed of the two methods were tested separately through simulation experiments. GMM -HMM achieves 74.1% accuracy and 14 (frame/s) running speed, while DNN -HMM achieves 80.6% accuracy and 9 (frame/s) running speed, DNN -HMM has higher accuracy and running speed to meet the real-time requirements for pipeline leakage audio detection.

Multi-microphone speech recognition integrating beamforming, robust feature extraction, and advanced DNN/RNN backend

Dialect Identification is the process of identifies the dialects of particular standard language. The Telugu Language is one of the historical and important languages. Like any other language Telugu also contains mainly three dialects Telangana, Costa Andhra and Rayalaseema. The research work in dialect identification is very less compare to Language identification because of dearth of database. In any dialects identification system, the database and feature engineering play vital roles because of most the words are similar in pronunciation and also most of the researchers apply statistical approaches like Hidden Markov Model (HMM), Gaussian Mixture Model (GMM), etc. to work on speech processing applications. But in today's world, neural networks play a vital role in all application domains and produce good results. One of the types of the neural networks is Deep Neural Networks (DNN) and it is used to achieve the state of the art performance in several fields such as speech recognition, speaker identification. In this, the Deep Neural Network (DNN) based model Multilayer Perceptron is used to identify the regional dialects of the Telugu Language using enhanced Mel Frequency Cepstral Coefficients (MFCC) features. To do this, created a database of the Telugu dialects with the duration of 5h and 45m collected from different speakers in different environments. The results produced by DNN model compared with HMM and GMM model and it is observed that the DNN model provides good performance.

In this paper, we present a model for Turkish speech recognition. The model is syllable-based, where the recognition is performed through syllables as speech recognition units. The main goal of the model is to recognize as much as possible of a given continuous speech by identifying only a small set of syllables in the language. For that purpose, only the syllable types with a higher frequency are selected for the recognition. The use of longer recognition units in speech recognition systems increases the success of the recognition since it is easier to detect the endpoints of syllables when compared to phonemes. On the other side, word-based recognition requires a very large dataset that includes all the words and word forms in the language, which is also another challenge. Hereby, we take the advantage of Turkish being an ortographically transparent and syllabified language. Our model employs time delay neural networks (TDNNs) for learning syllables. We achieve an accuracy of %65.6 on our large vocabulary continuous speech corpus. In addition, we define an algorithm for the automatic detection of syllable boundaries which gives an accuracy of %44. The automatic syllable boundary detection module is used for the recognition of isolated syllables rather than a continuous speech.