robust automatic speech recognition
An FFT-Based Companding Front End for Noise-Robust Automatic Speech Recognition
... on speech recognition tasks in noise is extraordinary compared to state-of-the-art auto- matic speech recognition (ASR) systems ...pattern recognition abili- ties not well captured by ...
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Attention based audio visual fusion for robust automatic speech recognition
... We begin by analysing our results on TCD-TIMIT. We first notice relative improvements starting at 7% on clean speech (17.7% CER down from 19.16%), up to 30% at -5db SNR (32.68% CER down from 46.52%) using our ...
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A Hybrid HMM/SVM Classifier for Wavelet Front End Robust Automatic Speech Recognition
... in automatic speech recognition (ASR) systems, they have been embedded in diversity of applications such as mobile devices, medical diagnosis, automotive vehicles, industry and military applications ...
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Robust Automatic Speech Recognition Features using Complex Wavelet Packet Transform Coefficients
... based Automatic Speech Recognition (ASR) in noisy environment; we developed a new technique that could add robustness to clean phonemes ...These robust features are obtained from Complex ...
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Sparse coding of the modulation spectrum for noise-robust automatic speech recognition
... the recognition perfor- mance with LDA-transformed features came as a surprise, not in the last place because we have seen that cluster purity increases after LDA ...clean speech suggests that the ...
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A Robust Observation Model for Automatic Speech Recognition with Adaptive Thresholding
... of speech signal and increase the word error rate of automatic speech recognition ...on speech signal. From those observations a relation between clean speech signal and ...
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A Robust Isolated Automatic Speech Recognition System using Machine Learning Techniques
... by speech recognition and language of speech is identified using Automated speech recognition (ASR) system and then in a respective natural language the segments of input speech ...
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Noise Cancellation Method for Robust Speech Recognition
... from speech signal. The degradation of speech due to presence of background noise and several other noises cause difficulties in various signal processing tasks like speech recognition, ...
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Quality Estimation for Automatic Speech Recognition
... In addition, it’s worth noting that when a QE model is trained and tested on data transcribed by the same ASR system the results are significantly better (the MAE is always about 1.0 - 6.0 points lower). Indeed, as also ...
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A Survey Paper on Automatic Speech Recognition by Machine
... for speech recognition, the cepstral coefficients derived from either linear prediction analysis or a filter-bank are found to be sensitive to additive noise ...for robust speech ...achieve ...
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Robust Features for Automatic Text-Independent Emotion Recognition from Speech
... Oudeyer 2002, Bosch 2003). Label predicated relegation endeavors are, however, prone to semantic mystification in the truth data and other quandaries in data accumulation (e.g. methodological issues in obtaining some ...
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Literature Review on Automatic Speech Recognition
... word speech recognition is the system where the words are separated by ...word speech recognition is a class of fluent speech strings where the set of strings is derived from ...
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Automatic Speech Recognition: A Shifted Role in Early Speech Intervention?
... When the use of Speech Viewer was restricted to the improvement of prosodic features of speech for children with hearing impairments, better results were produced. Öster conducted a study with two deaf ...
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Enhancing the magnitude spectrum of speech features for robust speech recognition
... resulting speech features are more ...each speech frame is amplified by multiplying by a weighting factor that is related to the signal-to-noise ratio ...the speech and non-speech frames, not ...
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Automatic speech recognition with deep neural networks for impaired speech
... Abstract. Automatic Speech Recognition has reached almost human performance in some controlled ...However, recognition of im- paired speech is a difficult task for two main reasons: ...
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A Review On Different Feature Recognition Techniques For Speech Process In Automatic Speech Recognition.
... communication. Speech processing is one of the most rousing research areas under signal ...hence speech processing can also be distinctively called as digital signal processing appertained to speech ...
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UCSY-SC1: A Myanmar speech corpus for automatic speech recognition
... a speech corpus which is developed for Myanmar Au- tomatic Speech Recognition (ASR) ...research. Automatic Speech Recognition (ASR) research has been conducted by the researchers ...
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Spectral Estimation for Noise Robust Speech Recognition
... SPECTRAL ESTIMATION FOR NOISE ROBUST SPEECH RECOGNITION SPECTRAL ESTIMATION FOR NOISE ROBUST SPEECH R E C O G N I T I O N Adoram Erell and Mitch Weintraub SRI International A B S T R A C T We present[.] ...
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Acoustical Pre Processing for Robust Speech Recognition
... ACOUSTICAL PRE PROCESSING FOR ROBUST SPEECH RECOGNITION ACOUSTICAL PRE PROCESSING FOR ROBUST SPEECH RECOGNITION Richard M Stern and Alejandro Acero 1 School of Computer Science Carnegie Mellon Univers[.] ...
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Efficient Cepstral Normalization for Robust Speech Recognition
... EFFICIENT CEPSTRAL NORMALIZATION FOR ROBUST SPEECH RECOGNITION EFFICIENT CEPSTRAL NORMALIZATION FOR ROBUST SPEECH R E C O G N I T I O N Fu Hua Liu, Richard M Stern, Xuedong Huang, Alejandro Acero Depa[.] ...
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