Online learning system for English speech automatic recognition based on hidden Markov model algorithm and conditional random field algorithm

With the further improvement of basic equipment, more and more data can be discovered and used. With the continuous improvement of computer computing power and the perfection of machine learning and other theories, speech recognition technology gradually tends to be practical. Research on the application of data mining in speech recognition and constructing an English translator speech recognition system has practical significance in our country. Therefore, this article mainly studies the application of data mining in the design of English translator speech recognition system. This paper analyzes the speech data of data mining technology, speech recognition technology and machine translation technology, and proposes a speech error correction algorithm based on the Jaro-winkler algorithm, which improves the accuracy of speech recognition. This paper designs a platform development plan for an English translator speech recognition system, and tests the translation performance of the system. The results show that the overall recognition rate of the system is 89.45%, and the translation accuracy rate is 91.64%. This shows that the accuracy of the system is high, and the input sentence can be translated more accurately, which meets the requirements of the English speech translation system in specific situations.

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%.

Challenges of Automatic Speech Recognition for medical interviews - research for Polish language

Combined speech enhancement and auditory modelling for robust distributed speech recognition

The aim of this study is to quantify the gap between the recognition performance of human listeners and an automatic speech recognition (ASR) system with special focus on intrinsic variations of speech, such as speaking rate and effort, altered pitch, and the presence of dialect and accent. Second, it is investigated if the most common ASR features contain all information required to recognize speech in noisy environments by using resynthesized ASR features in listening experiments. For the phoneme recognition task, the ASR system achieved the human performance level only when the signal-to-noise ratio (SNR) was increased by 15 dB, which is an estimate for the human-machine gap in terms of the SNR. The major part of this gap is attributed to the feature extraction stage, since human listeners achieve comparable recognition scores when the SNR difference between unaltered and resynthesized utterances is 10 dB. Intrinsic variabilities result in strong increases of error rates, both in human speech recognition (HSR) and ASR (with a relative increase of up to 120%). An analysis of phoneme duration and recognition rates indicates that human listeners are better able to identify temporal cues than the machine at low SNRs, which suggests incorporating information about the temporal dynamics of speech into ASR systems.

This study assesses the performance of a state-of-the-art automatic speech recognition (ASR) system at extracting target words in two different speech registers: infant-directed speech (IDS) and adult-directed speech (ADS). We used the Kaldi-NL ASR-service, developed by the Dutch Foundation of Open Speech Technology. The results indicate that the accuracy of the tool is much lower in IDS than in ADS. There are differences between IDS and ADS which negatively affect the performance of the existing ASR system. Therefore, new tools need to be developed for the automatic annotation of IDS. Nevertheless, the ASR system can already find more than half of the target words, which is promising.

These days, automatic speech recognition (ASR) systems achieve higher and higher accuracy rates. The score drops significantly, in case when the ASR system is being used with a non-native speaker of the language to be recognized. The main reason is specific pronunciation and accent features. A limited volume of labeled non-native speech datasets makes it difficult to train new ASR systems for non-native speakers.

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.

Results from digital and analog filter bank preprocessors are compared in order to establish the validity of analog processing for automatic speech recognition (ASR) systems. Three systems are evaluated using speaker and context independent phoneme recognition tasks. The three ASR systems are identical except for the preprocessing techniques used to derive three signal representations: extraction of (1) the digital mel-frequency spectrum, (2) the mel-frequency spectrum from commercial discrete bandpass filters and (3) the exponential spectrum from analog VLSI bandpass filter bank. The discrete analog system exhibits a 38% increase in recognition accuracy over the digital preprocessing technique. The digital and analog VLSI-based techniques perform comparably (within 3% of each other).

This paper describes open source software (available at https://github.com/robotology/natural-speech) to build automatic speech recognition (ASR) systems and run them within the YARP platform. The toolkit is designed (i) to allow non-ASR experts to easily create their own ASR system and run it on iCub and (ii) to build deep learning-based models specifically addressing the main challenges an ASR system faces in the context of verbal human–iCub interactions. The toolkit mostly consists of Python, C++ code and shell scripts integrated in YARP. As additional contribution, a second codebase (written in Matlab) is provided for more expert ASR users who want to experiment with bio-inspired and developmental learning-inspired ASR systems. Specifically, we provide code for two distinct kinds of speech recognition: “articulatory” and “unsupervised” speech recognition. The first is largely inspired by influential neurobiological theories of speech perception which assume speech perception to be mediated by brain motor cortex activities. Our articulatory systems have been shown to outperform strong deep learning-based baselines. The second type of recognition systems, the “unsupervised” systems, do not use any supervised information (contrary to most ASR systems, including our articulatory systems). To some extent, they mimic an infant who has to discover the basic speech units of a language by herself. In addition, we provide resources consisting of pre-trained deep learning models for ASR, and a 2.5-h speech dataset of spoken commands, the VoCub dataset, which can be used to adapt an ASR system to the typical acoustic environments in which iCub operates.

A low-power and real-time hidden Markov model (HMM) accelerator is proposed for gesture user interfaces on wearable smart devices. HMM algorithm is widely used for sequence recognitions such as speech recognition and gesture recognition due to its best-in-class recognition accuracy. However, the HMM algorithm incorporates high computational complexity and requires massive memory bandwidth for sequence matches. There have been studies on hardware acceleration of the HMM algorithm to resolve these issues, but they were focused on the speech recognition and did not incorporate the motion orientation capability required for the gesture recognition case. In this paper, we propose an HMM accelerator incorporating the motion orientation block for gesture recognitions on wearable devices. Binary search is exploited in the motion orientation to avoid the division and arctangent associated with the orientation and reduce its arithmetic complexity. In addition, gesture models are clustered in the gesture database to save the memory bandwidth by reducing memory transactions. Moreover, logarithmic arithmetic is used in Viterbi decoding in the HMM for more reduction in its complexity. Thanks to these schemes, this work achieves 25.6% power reduction compared with a plain hardware implementation of the gesture recognizing HMM.

In this study, a real-time automatic speech recognition (ASR) system based on the Kaldi ASR toolkit, with low-latency and customizable models, without any internet connection, was developed. The proposed ASR system includes a voice activity detection (VAD) module and an audio transmitter as a front-end speech processing and a decoder for the received audio signals. The ASR system was evaluated in terms of ASR accuracy and speech processing speed. Consequently, the ASR system achieved high ASR accuracy on the CSJ (Corpus of Spontaneous Japanese) test set with super low-latency.

Automatic Speech Recognition (ASR) systems convert spoken words into text and play a critical role in voice assistants, transcription services, and other accessibility tools. However, their reliance on machine learning models makes them susceptible to adversarial attacks. By adding small, often imperceptible perturbations to the input audio, or by convolving it with a carefully designed room impulse response, attackers can cause ASR systems to produce incorrect or even malicious transcripts. In this work, we propose an ensemble detection method for the efficient identification of targeted adversarial attacks in ASR systems. Specifically, we introduce MELo-FEST, which calculates the minimum energy in the low-frequency band of an audio signal to detect convolutive adversarial attacks. We then combine two spectrogram-based detection methods with a noise-adding approach to form an ensemble detector capable of identifying both additive and convolutive targeted adversarial attacks. Through extensive experiments, we demonstrate that our proposed ensemble detector can accurately identify adversarial audio generated by both non-adaptive and adaptive CW, PGD, and AdvReverb attacks in Wav2Vec2 and Whisper ASR systems, achieving an F1 score of at least 0.97. Moreover, our method performs both speech recognition and adversarial detection for each input audio sample in an average of under 0.13 seconds for Wav2Vec2 and 0.29 seconds for Whisper, making it well-suited for real-time applications. Additionally, we find that Whisper is more vulnerable than Wav2Vec2 to both non-adaptive and adaptive targeted adversarial attacks.

Performance evaluation of Hindi speech recognition system using optimized filterbanks

This paper describes a comparison between hybrid and end-to-end Automatic Speech Recognition (ASR) systems, which were evaluated on the IberSpeech-RTVE 2020 Speech-to-Text Transcription Challenge. Deep Neural Networks (DNNs) are becoming the most promising technology for ASR at present. In the last few years, traditional hybrid models have been evaluated and compared to other end-to-end ASR systems in terms of accuracy and efficiency. We contribute two different approaches: a hybrid ASR system based on a DNN-HMM and two state-of-the-art end-to-end ASR systems, based on Lattice-Free Maximum Mutual Information (LF-MMI). To address the high difficulty in the speech-to-text transcription of recordings with different speaking styles and acoustic conditions from TV studios to live recordings, data augmentation and Domain Adversarial Training (DAT) techniques were studied. Multi-condition data augmentation applied to our hybrid DNN-HMM demonstrated WER improvements in noisy scenarios (about 10% relatively). In contrast, the results obtained using an end-to-end PyChain-based ASR system were far from our expectations. Nevertheless, we found that when including DAT techniques, a relative WER improvement of 2.87% was obtained as compared to the PyChain-based system.