Automatic Speech Recognition Techniques Research Articles

Design and implementation of voice-command controller for fixed-wing unmanned aerial vehicles using automatic speech recognition and natural language processing techniques

This paper explores the development of a voice command controller leveraging the capabilities of an automatic speech recognition (ASR) system and natural language processing (NLP) technique to manage a fixed-wing unmanned aerial vehicle (UAV). The controller is designed to interpret voice commands for controlling fixed-wing UAVs. The implementation of the system involved two key stages: (1) implementation of a voice command controller using integrated ASR and NLP techniques deployed in a simulated plane in the SITL simulator followed by (2) deployment of the controller to an actual Sky Surfer plane fixed-wing aircraft. The results indicate that the algorithm achieved an average confidence rate of 91.86 % in transcribing voice commands to words, with a Word Error Rate (WER) of approximately 0.021. The developed system demonstrated the ability to interpret both low-level and high-level commands for UAV control interfaces. Such an interface offers greater intuitiveness compared to traditional RC controls, potentially requiring less training to operate effectively. Moreover, it reduces human workload, as once commands are issued, the system can execute them without the need for continuous supervision.

A Comparative Analysis of LSTM and Transformer-based Automatic Speech Recognition Techniques

A Comparative Analysis of LSTM and Transformer-based Automatic Speech Recognition Techniques

KMSAV: Korean multi‐speaker spontaneous audiovisual dataset

AbstractRecent advances in deep learning for speech and visual recognition have accelerated the development of multimodal speech recognition, yielding many innovative results. We introduce a Korean audiovisual speech recognition corpus. This dataset comprises approximately 150 h of manually transcribed and annotated audiovisual data supplemented with additional 2000 h of untranscribed videos collected from YouTube under the Creative Commons License. The dataset is intended to be freely accessible for unrestricted research purposes. Along with the corpus, we propose an open‐source framework for automatic speech recognition (ASR) and audiovisual speech recognition (AVSR). We validate the effectiveness of the corpus with evaluations using state‐of‐the‐art ASR and AVSR techniques, capitalizing on both pretrained models and fine‐tuning processes. After fine‐tuning, ASR and AVSR achieve character error rates of 11.1% and 18.9%, respectively. This error difference highlights the need for improvement in AVSR techniques. We expect that our corpus will be an instrumental resource to support improvements in AVSR.

An Eavesdropping System Based on Magnetic Side-Channel Signals Leaked by Speakers

The use of speakers in electronic devices has become widespread, but the security risks associated with micro-speakers, such as earphones, are often overlooked. Many assume that soundproof barriers can prevent sound leakage and protect privacy. This article presents the prototype MagEar, an eavesdropping system that exploits magnetic side-channel signals leaked by a micro-speaker to restore intelligible human speech. MagEar outperforms some high-precision magnetometers in detecting magnetic fields at the nanotesla level. Even at a distance of 60 cm, it can recover high-quality audio with a 90% similarity to the original audio. Moreover, the MagEar prototype is portable and can be concealed within a headset housing. We have implemented MagEar as a proof-of-concept system and conducted multiple case studies on the eavesdropping of various speaker-embedded devices, including earphones. The recovered speech can be transcribed using automatic speech recognition techniques, even when obstructed by soundproof walls. It is our aspiration that our work can prompt manufacturers to reconsider the security vulnerabilities of speakers.

OAVA: the open audio-visual archives aggregator

The purpose of the current article is to provide an overview of an open-access audiovisual aggregation and search service platform developed for Greek audiovisual content during the OAVA (Open Access AudioVisual Archive) project. The platform allows the search of audiovisual resources utilizing metadata descriptions, as well as full-text search utilizing content generated from automatic speech recognition (ASR) processes through deep learning models. A dataset containing reliable Greek audiovisual content providers and their resources (1710 in total) is created. Both providers and resources are reviewed according to specific criteria already established and used for content aggregation purposes, to ensure the quality of the content and to avoid copyright infringements. Well-known aggregation services and well-established schemas for audiovisual resources have been studied and considered regarding both aggregated content and metadata. Most Greek audiovisual content providers do not use established metadata schemas when publishing their content, nor technical cooperation with them is guaranteed. Thus, a model is developed for reconciliation and aggregation. To utilize audiovisual resources the OAVA platform makes use of the latest state-of-the-art ASR approaches. OAVA platform supports Greek and English speech-to-text models. Specifically for Greek, to mitigate the scarcity of available datasets, a large-scale ASR dataset is annotated to train and evaluate deep learning architectures. The result of the above-mentioned efforts, namely selection of content, metadata, development of appropriate ASR techniques, and aggregation and enrichment of content and metadata, is the OAVA platform. This unified search mechanism for Greek audiovisual content will serve teaching, research, and cultural activities. OAVA platform is available at: https://openvideoarchives.gr/.

Predicting Transitions from Mild Cognitive Impairment to Dementia within 5‐years

AbstractBackgroundEarly detection of individuals at high risk of future progression toward dementia is in great need, particularly in the era of emerging treatments for Alzheimer’s disease. Given the easier administration of neuropsychological (NP) tests over invasive and expensive biomarker acquisition, we seek to predict the future cognitive status of an individual based on digital voice recordings of NP exams.MethodWe utilized automatic speech recognition and natural language processing techniques to create an automated tool that predicts if an individual with mild cognitive impairment (MCI) will develop dementia in a fixed follow‐up window (5 years). We characterize the predictive accuracy of this approach using data from the Framingham Heart Study. First, the automated transcription of the digital voice recordings were classified into 8 main NP sub‐tests including memory assessment, naming and language skill, verbal fluency, general questions, etc. Using a large language model, only the participants’ sentences were encoded into quantitative data. These data and the participants’ characteristics, such as age, sex, ApoE4 (+/‐) genotype, and education were employed to train and test a binary classifier, predicting dementia withing 5 years after a positive MCI diagnosis. We also provide a comparison between our approach and alternative models using demographics and ApoE genotype or the Mini‐Mental State Examination (MMSE) score.ResultWe evaluated the performance of the classification task on the data containing 111 stable MCI and 103 progressive MCI subjects. Our method achieved an average accuracy of 75.5% on the held‐out test data while a baseline model using age, sex, education, and ApoE has an average accuracy of 71.8%. The sensitivity and specificity of the proposed method versus the baseline model are 78.2% vs. 81.8% and 72.7% vs. 61.8%, respectively. Our approach also significantly outperforms the model based on the MMSE score by a margin of 14% in accuracy.ConclusionWe have analyzed digital voice recordings of NP exams for participants with MCI to predict their cognitive status in 5 years. The proposed approach offers a fully automated procedure with higher accuracy, providing an opportunity to develop an accessible and easy‐to‐administer screening tool that could be easily adapted to any language.

Lexical and syntactic deficits analyzed via automated natural language processing: the new monitoring tool in multiple sclerosis.

Impairment of higher language functions associated with natural spontaneous speech in multiple sclerosis (MS) remains underexplored. We presented a fully automated method for discriminating MS patients from healthy controls based on lexical and syntactic linguistic features. We enrolled 120 MS individuals with Expanded Disability Status Scale ranging from 1 to 6.5 and 120 age-, sex-, and education-matched healthy controls. Linguistic analysis was performed with fully automated methods based on automatic speech recognition and natural language processing techniques using eight lexical and syntactic features acquired from the spontaneous discourse. Fully automated annotations were compared with human annotations. Compared with healthy controls, lexical impairment in MS consisted of an increase in content words (p = 0.037), a decrease in function words (p = 0.007), and overuse of verbs at the expense of noun (p = 0.047), while syntactic impairment manifested as shorter utterance length (p = 0.002), and low number of coordinate clause (p < 0.001). A fully automated language analysis approach enabled discrimination between MS and controls with an area under the curve of 0.70. A significant relationship was detected between shorter utterance length and lower symbol digit modalities test score (r = 0.25, p = 0.008). Strong associations between a majority of automatically and manually computed features were observed (r > 0.88, p < 0.001). Automated discourse analysis has the potential to provide an easy-to-implement and low-cost language-based biomarker of cognitive decline in MS for future clinical trials.

Using Automatic Speech Recognition to Assess Thai Speech Language Fluency in the Montreal Cognitive Assessment (MoCA).

The Montreal cognitive assessment (MoCA), a widely accepted screening tool for identifying patients with mild cognitive impairment (MCI), includes a language fluency test of verbal functioning; its scores are based on the number of unique correct words produced by the test taker. However, it is possible that unique words may be counted differently for various languages. This study focuses on Thai as a language that differs from English in terms of word combinations. We applied various automatic speech recognition (ASR) techniques to develop an assisted scoring system for the MoCA language fluency test with Thai language support. This was a challenge because Thai is a low-resource language for which domain-specific data are not publicly available, especially speech data from patients with MCIs. Furthermore, the great variety of pronunciation, intonation, tone, and accent of the patients, all of which might differ from healthy controls, bring more complexity to the model. We propose a hybrid time delay neural network hidden Markov model (TDNN-HMM) architecture for acoustic model training to create our ASR system that is robust to environmental noise and to the variation of voice quality impacted by MCI. The LOTUS Thai speech corpus was incorporated into the training set to improve the model’s generalization. A preprocessing algorithm was implemented to reduce the background noise and improve the overall data quality before feeding data into the TDNN-HMM system for automatic word detection and language fluency score calculation. The results show that the TDNN-HMM model in combination with data augmentation using lattice-free maximum mutual information (LF-MMI) objective function provides a word error rate (WER) of 30.77%. To our knowledge, this is the first study to develop an ASR with Thai language support to automate the scoring system of MoCA’s language fluency assessment.

Improving low-resource Tibetan end-to-end ASR by multilingual and multilevel unit modeling

Conventional automatic speech recognition (ASR) and emerging end-to-end (E2E) speech recognition have achieved promising results after being provided with sufficient resources. However, for low-resource language, the current ASR is still challenging. The Lhasa dialect is the most widespread Tibetan dialect and has a wealth of speakers and transcriptions. Hence, it is meaningful to apply the ASR technique to the Lhasa dialect for historical heritage protection and cultural exchange. Previous work on Tibetan speech recognition focused on selecting phone-level acoustic modeling units and incorporating tonal information but underestimated the influence of limited data. The purpose of this paper is to improve the speech recognition performance of the low-resource Lhasa dialect by adopting multilingual speech recognition technology on the E2E structure based on the transfer learning framework. Using transfer learning, we first establish a monolingual E2E ASR system for the Lhasa dialect with different source languages to initialize the ASR model to compare the positive effects of source languages on the Tibetan ASR model. We further propose a multilingual E2E ASR system by utilizing initialization strategies with different source languages and multilevel units, which is proposed for the first time. Our experiments show that the performance of the proposed method-based ASR system exceeds that of the E2E baseline ASR system. Our proposed method effectively models the low-resource Lhasa dialect and achieves a relative 14.2% performance improvement in character error rate (CER) compared to DNN-HMM systems. Moreover, from the best monolingual E2E model to the best multilingual E2E model of the Lhasa dialect, the system’s performance increased by 8.4% in CER.

Semantic Segmentation of Text Using Deep Learning

Given a text, can we segment it into semantically coherent sections in an automatic way? Can we detect the semantic boundaries, if we know how many they are? Can we determine how many semantically distinct sections are in the text? These are the questions we address in this paper. To respond, we use the Bidirectional Encoder Representation from Transformer (BERT) to analyze the text and evaluate a function that we call local incoherence, which we expect to show maxima at the points where a semantic boundary is detected. Our results, although preliminary, are encouraging and suggest that our approach can be successfully applied. However, they are quite sensitive with respect to the text quality, as it happens in the case in which the text is derived from an audio stream via Automatic Speech Recognition techniques.

ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems

In this paper, a multilingual end-to-end framework, called ATCSpeechNet, is proposed to tackle the issue of translating communication speech into human-readable text in air traffic control (ATC) systems. In the proposed framework, we focus on integrating multilingual automatic speech recognition (ASR) into one model, in which an end-to-end paradigm is developed to convert speech waveforms into text directly, without any feature engineering or lexicon. To compensate the deficiency of handcrafted feature engineering caused by ATC challenges, including multilingual, multispeaker dialog and unstable speech rates, a speech representation learning (SRL) network is proposed to capture robust and discriminative speech representations from raw waves. The self-supervised training strategy is adopted to optimize the SRL network from unlabeled data, and to further predict the speech features, i.e., wave-to-feature. An end-to-end architecture is improved to complete the ASR task, in which a grapheme-based modeling unit is applied to address the multilingual ASR issue. Facing the problem of small transcribed samples in the ATC domain, an unsupervised approach with mask prediction is applied to pretrain the backbone network of the ASR model on unlabeled data by a feature-to-feature process. Finally, by integrating the SRL with ASR, an end-to-end multilingual ASR framework is formulated in a supervised manner, which is able to translate the raw wave into text in one model, i.e., wave-to-text. Experimental results on the ATCSpeech corpus demonstrate that the proposed approach achieves high performance with a very small labeled corpus and less resource consumption, only a 4.20% label error rate on the 58-hour transcribed corpus. Compared to the baseline model, the proposed approach obtains over 100% relative performance improvement which can be further enhanced with increasing size of the transcribed samples. It is also confirmed that the proposed SRL and training strategies make significant contributions to improving the final performance. In addition, the effectiveness of the proposed framework is also validated on common corpora (AISHELL, LibriSpeech, and cv-fr). More importantly, the proposed multilingual framework not only reduces the system complexity but also obtains higher accuracy compared to that of the independent monolingual ASR models. The proposed approach can also greatly reduce the cost of annotating samples, which benefits to advance the ASR technique to industrial applications.

SEMANTIC SURVEY ON AUTOMATIC SPEECH RECOGNITION SYSTEM FOR REGIONAL LANGUAGES

Speech recognition in regional languages has become one of the most important researchable area which employs as a helping mechanism to talk and understand in our own language. The Automatic speech recognition system has played an important role for the people who have hearing and linguistic problem. Speech recognition has become a realistic technology that is now being applied in numerous languages around the world and is being used in applications of real-life human language, such as knowledge recovery. In this paper various parameters including development of Automatic Speech Recognition systems, tools and techniques used to construct an ASR system with their advantages and limitations and research on system of regional languages with ASR have been discussed. The main motive of this work is to present various techniques used for speech extraction, recognition and subtitle generation.

Looseness detection in cup-lock scaffolds using percussion-based method

Across multiple construction processes, the cup-lock scaffolding systems have been widely applied as temporary facilities, while several catastrophes caused by scaffolding collapse have been reported. Therefore, in this paper, we conduct an exploratory study to attempt to research one issue that can affect the stability of scaffolding systems, namely the looseness of the cup-lock joint. In this paper, to detect looseness of cup-lock scaffold, we develop a new percussion-based method to avoid current structural health monitoring (SHM) methods that depend on constant contact between structures and sensors. Particularly, inspired by the rapid development of automatic speech recognition (ASR), we propose a convolutional bi-directional long short-term memory (CBLSTM) model to classify the Mel frequency cepstral coefficient (MFCC) features extracted from percussion-induced sound signals. To the best of our knowledge, this is the first application of ASR technique in looseness detection of cup-lock scaffold. The working mechanism of CBLSTM is given as follows: a convolutional neural network (CNN) is used to craft characteristics from MFCC features, and a bi-directional long short-term memory architecture (BLSTM) can improve classification accuracy by assimilating the learned CNN features. Finally, a laboratory experiment is conducted to verify the effectiveness of the proposed method, and we demonstrate that CBLSTM outperforms the CNN and BLSTM in classifying the MFCC features. Overall, the percussion-based method proposed in this paper can provide a new direction for the investigation, particularly health monitoring, on the cup-lock scaffolding system.

End-to-End Automatic Pronunciation Error Detection Based on Improved Hybrid CTC/Attention Architecture.

Advanced automatic pronunciation error detection (APED) algorithms are usually based on state-of-the-art automatic speech recognition (ASR) techniques. With the development of deep learning technology, end-to-end ASR technology has gradually matured and achieved positive practical results, which provides us with a new opportunity to update the APED algorithm. We first constructed an end-to-end ASR system based on the hybrid connectionist temporal classification and attention (CTC/attention) architecture. An adaptive parameter was used to enhance the complementarity of the connectionist temporal classification (CTC) model and the attention-based seq2seq model, further improving the performance of the ASR system. After this, the improved ASR system was used in the APED task of Mandarin, and good results were obtained. This new APED method makes force alignment and segmentation unnecessary, and it does not require multiple complex models, such as an acoustic model or a language model. It is convenient and straightforward, and will be a suitable general solution for L1-independent computer-assisted pronunciation training (CAPT). Furthermore, we find that in regards to accuracy metrics, our proposed system based on the improved hybrid CTC/attention architecture is close to the state-of-the-art ASR system based on the deep neural network–deep neural network (DNN–DNN) architecture, and has a stronger effect on the F-measure metrics, which are especially suitable for the requirements of the APED task.

Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition.

Artificial neural networks (ANN) have become the mainstream acoustic modeling technique for large vocabulary automatic speech recognition (ASR). A conventional ANN features a multi-layer architecture that requires massive amounts of computation. The brain-inspired spiking neural networks (SNN) closely mimic the biological neural networks and can operate on low-power neuromorphic hardware with spike-based computation. Motivated by their unprecedented energy-efficiency and rapid information processing capability, we explore the use of SNNs for speech recognition. In this work, we use SNNs for acoustic modeling and evaluate their performance on several large vocabulary recognition scenarios. The experimental results demonstrate competitive ASR accuracies to their ANN counterparts, while require only 10 algorithmic time steps and as low as 0.68 times total synaptic operations to classify each audio frame. Integrating the algorithmic power of deep SNNs with energy-efficient neuromorphic hardware, therefore, offer an attractive solution for ASR applications running locally on mobile and embedded devices.

QCNN Inspired Reconfigurable Keyword Spotting Processor With Hybrid Data-Weight Reuse Methods

The keyword spotting (KWS) system is one of the most important interfaces between humans and machines since it is usually the start of automatic speech recognition and natural language processing techniques. However, for KWS hardware, it is still a problem to make one specified chip both low power and high performed under multiple scenarios, such as in meeting rooms, on different traffic or in parks and so on, for different scenarios own wide range signal-noise-ratios (SNRs). The problem leads to the requirements of balanced design between KWS system accuracy and the hardware cost under various noise types and levels. To overcome the balanced design and tradeoff problems, a complete KWS processor including an Mel-Frequency Cepstrum Coefficients (MFCC) feature extractor and a quantized Convolutional Neural Network (QCNN) accelerator is proposed for wide SNR range and low-power KWS in this paper. Firstly, the approach to quantize CNNs into QCNNs with high accuracy is proposed with considerations of hardware-software tradeoff. With the tradeoff of KWS system accuracy and hardware cost, the 4bit/8bit dual-working-mode strategy is proposed to keep low hardware cost and high accuracy under different scenarios. To be specific, the training, tuning and validating of the CNNs and QCNNs are taken with the dataset of 10 keywords chosen from the Google Command Speech Dataset (GCSD). Secondly, a serial FFT based MFCC extractor is implemented with low power and small footprint. Finally, with a novel hybrid reuse strategy of input data and network weight, a reconfigurable and approximate computing based QCNN accelerator is designed. Implemented and verified under TSMC 22nm ULL technology, with the area of 1.42mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , the QCNN accelerator can achieve 5.26μW/9.08μW power consumption in 4bit/8bit work mode with accuracy of 88% and 93% respectively, which is superior to the state-of-the-art processors.

Real-Time Voice Transmission over Wireless Sensor Network (VoWSN) based Automatic Speech Recognition (ASR) Technique

Real-Time Voice Transmission over Wireless Sensor Network (VoWSN) based Automatic Speech Recognition (ASR) Technique

A Study on Utilization of Three-Dimensional Sensor Lip Image for Developing a Pronunciation Recognition System

Abstract The acoustic-based automatic speech recognition (ASR) technique has been a matured technique and widely seen to be used in numerous applications. However, acoustic-based ASR will not maintain a standard performance for the disabled group with an abnormal face, that is atypical eye or mouth geometrical characteristics. For governing this problem, this article develops a three-dimensional (3D) sensor lip image based pronunciation recognition system where the 3D sensor is efficiently used to acquire the action variations of the lip shapes of the pronunciation action from a speaker. In this work, two different types of 3D lip features for pronunciation recognition are presented, 3D-(x, y, z) coordinate lip feature and 3D geometry lip feature parameters. For the 3D-(x, y, z) coordinate lip feature design, 18 location points, each of which has 3D-sized coordinates, around the outer and inner lips are properly defined. In the design of 3D geometry lip features, eight types of features considering the geometrical space characteristics of the inner lip are developed. In addition, feature fusion to combine both 3D-(x, y, z) coordinate and 3D geometry lip features is further considered. The presented 3D sensor lip image based feature evaluated the performance and effectiveness using the principal component analysis based classification calculation approach. Experimental results on pronunciation recognition of two different datasets, Mandarin syllables and Mandarin phrases, demonstrate the competitive performance of the presented 3D sensor lip image based pronunciation recognition system.

Addressing noise and pitch sensitivity of speech recognition system through variational mode decomposition based spectral smoothing

In this paper, we propose a novel front-end speech parameterization technique for automatic speech recognition (ASR) that is less sensitive towards ambient noise and pitch variations. First, using variational mode decomposition (VMD), we break up the short-time magnitude spectrum obtained by discrete Fourier transform into several components. In order to suppress the ill-effects of noise and pitch variations, the spectrum is then sufficiently smoothed. The desired spectral smoothing is achieved by discarding the higher-order variational mode functions and reconstructing the spectrum using the first-two modes only. As a result, the smoothed spectrum closely resembles the spectral envelope. Next, the Mel-frequency cepstral coefficients (MFCC) are extracted using the VMD-based smoothed spectra. The proposed front-end acoustic features are observed to be more robust towards ambient noise and pitch variations than the conventional MFCC features as demonstrated by the experimental evaluations presented in this study. For this purpose, we developed an ASR system using speech data from adult speakers collected under relatively clean recording conditions. State-of-the-art acoustic modeling techniques based on deep neural networks (DNN) and long short-term memory recurrent neural networks (LSTM-RNN) were employed. The ASR systems were then evaluated under noisy test conditions for assessing the noise robustness of the proposed features. To assess robustness towards pitch variations, experimental evaluations were performed on another test set consisting of speech data from child speakers. Transcribing children's speech helps in simulating an ASR task where pitch differences between training and test data are significantly large. The signal domain analyses as well as the experimental evaluations presented in this paper support our claims.

Automatic recognition of emotions in the description of motion pictures for television archives

The information departments of television networks are undergoing a continuous automation of their document processes. Under the framework of image description, there is the possibility of implementing Automatic Speech Recognition (ASR) techniques and images, which can be used to identify the various emotions that can be seen in images. Journalists require information from connoted information and that is also explicitly present through facial gestures and voice vibration, so audiovisual information departments must describe those elements or find automated tools to help the identification thereof. In this paper, we present the method and validity of automating the processes for extracting information from emotions using biometric techniques. For this, we have conducted a bibliographical review and visited television information centers to determine the requirements, to then capture the necessary changes in the mechanisms of system automation.