Limitations in speech recognition for young adults with down syndrome

RETRACTED: Real time speech recognition algorithm on embedded system based on continuous Markov model

The accurate recognition of speech is beneficial to the fields of machine translation and intelligent human–computer interaction. After briefly introducing speech recognition algorithms, this study proposed to recognize speech with a recurrent neural network (RNN) and adopted the connectionist temporal classification (CTC) algorithm to align input speech sequences and output text sequences forcibly. Simulation experiments compared the RNN-CTC algorithm with the Gaussian mixture model–hidden Markov model and convolutional neural network-CTC algorithms. The results demonstrated that the more training samples the speech recognition algorithm had, the higher the recognition accuracy of the trained algorithm was, but the training time consumption increased gradually; the more samples a trained speech recognition algorithm had to test, the lower the recognition accuracy and the longer the testing time. The proposed RNN-CTC speech recognition algorithm always had the highest accuracy and the lowest training and testing time among the three algorithms when the number of training and testing samples was the same.

Speech recognition is one of the important areas in digital speech processing. The study of speech recognition is a part of a quest for artificially intelligent machines that can hear and understand spoken information. The conventional methods for speech recognition are very complicated and time consuming. To apply fuzzy logic to speech recognition is a new attempt in digital speech processing. The approach proposed in the paper simplifies the algorithm in speech recognition and makes the real-time processing time shorter. The situation considered in this paper is the simplest, i.e., the situation of speaker dependence, small vocabulary and isolated words.

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.

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.

Algorithms for speech recognition can be characterized broadly as pattern recognition approaches and acoustic phonetic approaches. To date, the greatest degree of success in speech recognition has been obtained using pattern recognition paradigms. Thus, in this paper, we will be concerned primarily with showing how pattern recognition techniques have been applied to the problems of isolated word (or discrete utterance) recognition, connected word recognition, and continuous speech recognition. We will show that our understanding (and consequently the resulting recognizer performance) is best for the simplest recognition tasks and is considerably less well developed for large scale recognition systems.

There have been higher demands recently for Automatic Speech Recognition (ASR) systems able to operate robustly in acoustically noisy environments. This paper proposes a method to effectively integrate audio and visual information in audio-visual (bi-modal) ASR systems. Such integration inevitably necessitates modeling of the synchronization of the audio and visual information. To address the time lag and correlation problems in individual features between speech and lip movements, we introduce a type of integrated HMM modeling of audio-visual information based on a family of HMM composition. The proposed model can represent state synchronicity not only within a phoneme but also between phonemes. Furthermore, we also propose a rapid stream weight optimization based on GPD algorithm for noisy bi-modal speech recognition. Evaluation experiments show that the proposed method improves the recognition accuracy for noisy speech. In SNR=0dB our proposed method attained 16% higher performance compared to a product HMMs without the synchronicity re-estimation.

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

Audio–visual speech recognition based on regulated transformer and spatio–temporal fusion strategy for driver assistive systems

In this paper, based on the study of two speech recognition algorithms, two designs of speech recognition system are given to realize this isolated speech recognition mobile robot control system based on ARM9 processor. The speech recognition process includes pretreatment of speech signal, characteristic extrication, pattern matching and post-processing. Mel-Frequency cepstrum coefficients (MFCC) and linear prediction cepstrum coefficients (LPCC) are the two most common parameters. Through analysis and comparison the parameters, MFCC shows more noise immunity than LPCC, so MFCC is selected as the characteristic parameters. Both dynamic time warping (DTW) and hidden markov model (HMM) are commonly used algorithm. For the different characteristics of DTW and HMM recognition algorithm, two different programs were designed for mobile robot control system. The effect and speed of the two speech recognition system were analyzed and compared.

In methods and apparatus for at least partially automating a telephone directory assistance function, directory assistance callers are prompted to speak locality or called entity names associated with desired directory listings. A speech recognition algorithm is applied to speech signals received in response to prompting to determine spoken locality or called entity names. Desired telephone numbers are released to callers, and released telephone numbers are used to confirm or correct at least some of the recognized locality or called entity names. Speech signal representations labelled with the confirmed or corrected names are used as labelled speech tokens to refine prior training of the speech recognition algorithm. The training refinement automatically adjusts for deficiencies in prior training of the speech recognition algorithm and to long term changes in the speech patterns of directory assistance callers served by a particular directory assistance installation. The methods can be generalized to other speech recognition applications.

This paper describes a new algorithm for speech recognition by using stereo vision pattern recognition equations with competition and cooperation. In our research, we applied recently developed 3-layered neural net (3LNN) equations to speech recognition. Our proposed acoustic models using these equations yield better recognition results than the hidden Markov model (HMM). When using a 216 (240) word database, stereo vision acoustic models gave 6.5% (6.6%) higher accuracy than HMMs.

The impact of non-native English speakers’ phonological and prosodic features on automatic speech recognition accuracy

Speech recognition technology is a allow the machine to understand human language and speech recognition algorithm is to achieve this function, the system by using HTK toolbox to design a speech recognition system, and in-depth research and Simulation of correlation algorithm, has completed the speech recognition test, the overall recognition rate reached 98%, to achieve the desired results, reflecting the higher practical application value.

Conventional speaker identification and speech recognition algorithms cannot deal with noisy and multiple speaker environments. For example, IBM via Voice has low recognition rates if dictation is done in a noisy environment. In order to achieve high performance in speaker identification and speech recognition, we propose an integrated approach that takes every facet of the process into account. Here we summarize some preliminary results from the application of this integrated approach to robust speaker identification and speech recognition. A real-time stand-alone software prototype has been developed to evaluate the effectiveness of the approach.