Automatic Speech Recognition Models, Tools, and Techniques

Robust speech recognition in reverberant environments by using an optimal synthetic room impulse response model

Speech recognition is an important problem of pattern recognition area. It has gained popularity with the advancements in technology. Therefore, large number of researches has been done in recent years to achieve the human-like accuracy in this field. Arabic speech was neglected in past years due to inherent difficulty and complexity in it. In recent years, many researchers worked on it and most of them focused on Qur'anic database as its being the standard of formal Arabic. Need is felt for an automatic Qur'an recognizer which could help Muslims to memorise Qur'an without human involvement. This paper focuses on this issue and identifies the major differences between simple Automatic Speech Recognition (ASR) system and a language tutor using ASR technology with respect to Qur'an memorisation. The system is implemented using ASR technique which has proven to be incomplete method for an intelligent Qur'an tutor and thus need modification to accomplish such task.

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

BackgroundAlzheimer’s disease (AD) is a progressive neurodegenerative disease. Like for other dementias, biomarkers may help characterize distinct aspects of the underlying pathology, predict decline, and monitor disease progression. We extracted a composite array of 24 speech‐based biomarkers using machine learning and signal processing techniques. We then determined which of the given biomarkers was predictive of MCI and AD progression from baseline by conducting a correlation analysis.MethodSpoken responses to a Cookie Theft Picture description task were recorded from 2 subjects with AD, 4 subjects with Mild Cognitive Impairment (MCI) due to AD (biomarker confirmed AD), and 4 subjects with MCI. We automatically extracted acoustic, linguistic, and cognitive biomarkers using speech recognition, acoustic, and language modeling techniques. We later computed Kendall’s tau‐b (tb) correlation between the biomarker values and Montreal Cognitive Assessment (MoCA) scores. Recordings and MoCA scores were collected at baseline, at 6 months after baseline, and at 12 months for a subgroup of the subjects. To perform the correlation analysis, we used scipy.stats.kendalltau library in Python.ResultWith respect to the acoustic biomarkers, a strong negative correlation (tb = ‐0.50) was observed between the MoCA scores and features such as pause time, pause percentage, and pause speech ratio, while a moderate positive correlation (tb = 0.2) was found for rhythm standard deviation. With respect to the linguistic biomarkers a strong positive correlation(tb = 0.50) for the features of corrected type‐token ratio, average sentence length in words, and noun count was reported, while a moderate positive correlation (tb = 0.27) was found for the features of word type count, word token count, and moving average type‐token ratio. The other acoustic, linguistic, and cognitive biomarkers showed a very weak (tb = 0.00 ‐ 0.10) or weak correlation (tb = 0.10 ‐ 0.20).ConclusionAltogether, these results suggest that speech‐based interpretable biomarkers may help clinicians to diagnose AD at earlier stages and monitor disease progression. Our preliminary data suggest that AD patients encounter more problems delivering longer and linguistically elaborated narratives as the disease progresses.

Recently, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel dereverberation techniques, and automatic speech recognition (ASR) techniques robust to reverberation. To evaluate state-of-the-art algorithms and obtain new insights regarding potential future research directions, we propose a common evaluation framework including datasets, tasks, and evaluation metrics for both speech enhancement and ASR techniques. The proposed framework will be used as a common basis for the REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge. This paper describes the rationale behind the challenge, and provides a detailed description of the evaluation framework and benchmark results.

Automatic speech recognition (ASR) is one of the major research field in computer science. ASR is one of the most necessary tools, which is required for, almost every languages. But every language has its own techniques and highlights. In past years, significant number of mentionable research has already been done in this field. And the exactness of ASR mostly depends on the noise reduction process. This is the fundamental requirement of Speech data preprocessing . There are various way of data preprocessing for speech recognition. In this paper we discuss very effective way for preprocessing technique of Bangla speech data. We demonstrate the whole process of recording, then digitizing the data, removing noise with respect to signal (SNR), transforming the signal into Fourier transformation (FFT) for time domain to frequency domain, it would be better if result frames are chunked into small window, and finally apply Mel frequency cepstral coefficients (MFCC) algorithm to make data into respective vector representation so that, data is ready for training in neural network.

Turkish speech corpora and recognition tools developed by porting SONIC: Towards multilingual speech recognition

Speech disorders are human disabilities widely present in young population but also adults may suffer from such disorders after some physical problems. In this context, the detection and further the correction of such disabilities may be handled by Automatic Speech Recognition (ASR) technology. The first works on the speech disorders detection began early in the 70s and seem to follow the same evolution as those on the ASR. Indeed, these early works were more based on the signal processing techniques. Progressively, systems dealing with speech disorders incorporate more ideas from ASR technology. Particularly, Hidden Markov Models, the state-of-the-art approaches in ASR systems, are used. This chapter reviews systems that use ASR techniques to evaluate pronunciation of people who suffer from speech or voice impairments. The authors investigate the existing systems and present the main innovation and some of the available resources.

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.

This research introduces a comprehensive strategy to enhance the performance of an existing automatic speech recognition (ASR) model, which has been previously documented in published articles. The study sets out to achieve several objectives. Firstly, it concentrates on updating the ASR model by retraining it with new datasets. This involves integrating samples from the latest Common Voice corpus release and data collected independently via the armspeech.com web application. Another key focus lies in optimizing the ASR model for near-real-time processing, intending to improve its speed and efficiency. The proposed adjustments to the model’s architecture aim to balance accuracy and processing speed, which is essential for applications requiring prompt speech recognition. Furthermore, the research explores the integration of Transformer models into the post-processing pipeline to introduce punctuation and capitalization into the ASR output. This step not only enhances the linguistic quality of transcriptions but also improves their readability and usability. In tandem with these advancements, the research presents a systematic approach to gathering, annotating, and storing datasets specifically tailored for punctuation and capitalization tasks. The methodology outlines the acquisition and organization of a dataset conducive to training Transformer models for these linguistic tasks. This comprehensive approach, which encompasses dataset enrichment, architectural modifications, and post-processing enhancements, aims to elevate the ASR model’s accuracy, speed, and linguistic refinement, with a particular focus on addressing the intricacies of the Armenian language. The research contributes valuable insights into the optimization of ASR systems, tackling both language-specific challenges and broader issues related to linguistic post-processing.

The automatic recognition of MP3 compressed speech presents a challenge to the current systems due to the lossy nature of compression which causes irreversible degradation of the speech wave. This article evaluates the performance of a recognition system optimized for MP3 compressed speech with current state-of-the-art acoustic modelling techniques and one specific front-end compensation method. The article concentrates on acoustic model adaptation, discriminative training, and additional dithering as prominent means of compensating for the described distortion in the task of phoneme and large vocabulary continuous speech recognition (LVCSR). The experiments presented on the phoneme task show a dramatic increase of the recognition error for unvoiced speech units as a direct result of compression. The application of acoustic model adaptation has proved to yield the highest relative contribution while the gain of discriminative training diminished with decreasing bit-rate. The application of additional dithering yielded a consistent improvement only for the MFCC features, but the overall results were still worse than those for the PLP features.

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.

This chapter presents Bangla (widely known as Bengali) Automatic Speech Recognition (ASR) techniques by evaluating the different speech features, such as Mel Frequency Cepstral Coefficients (MFCCs), Local Features (LFs), phoneme probabilities extracted by time delay artificial neural networks of different architectures. Moreover, canonicalization of speech features is also performed for Gender-Independent (GI) ASR. In the canonicalization process, the authors have designed three classifiers by male, female, and GI speakers, and extracted the output probabilities from these classifiers for measuring the maximum. The maximization of output probabilities for each speech file provides higher correctness and accuracies for GI speech recognition. Besides, dynamic parameters (velocity and acceleration coefficients) are also used in the experiments for obtaining higher accuracy in phoneme recognition. From the experiments, it is also shown that dynamic parameters with hybrid features also increase the phoneme recognition performance in a certain extent. These parameters not only increase the accuracy of the ASR system, but also reduce the computation complexity of Hidden Markov Model (HMM)-based classifiers with fewer mixture components.

This paper proposes a practical speech detection technique for robust automatic speech recognition, suitable for use under various interference conditions. This technique consists of a dual microphone system and an algorithm for processing their signals. The microphone module is placed in the workplace of the target speaker. The module consists of two symmetrical supercardioid microphones directed in opposite directions. The algorithm of target speaker detection is proposed for this scheme. This algorithm makes it possible to implement spatial filtering of speakers. Experiments with real recordings demonstrate a significant reduction of speech recognition errors for the target speaker due to suppression of acoustic crosstalk. The main advantage of the proposed technique is simplicity of its use in a wide range of practical situations.

Although over 300M around the world speak Bangla, scant work has been done in improving Bangla voice-to-text transcription due to Bangla being a low-resource language. However, with the introduction of the Bengali Common Voice 9.0 speech dataset, Automatic Speech Recognition (ASR) models can now be significantly improved. With 399hrs of speech recordings, Bengali Common Voice is the largest and most diversified open-source Bengali speech corpus in the world. In this paper, we outperform the State-of-the-Art (SOTA) pretrained Bengali ASR models by finetuning a pretrained wav2vec2 model on the common voice dataset. We also demonstrate how to significantly improve the performance of an ASR model by adding an n-gram language model as a post-processor. Finally, we do some experiments and hyperparameter tuning to generate a robust Bangla ASR model that is better than the existing ASR models.