Learning temporal patterns among primitive speech sequences and being able to control the motor apparatus for effective production of the learned patterns are imperative for speech acquisition in infants. In this paper, we develop a predictive coding model whose objective is to minimize the sensory (auditory) and proprioceptive prediction errors. Temporal patterns are learned by minimizing the ...

In this paper, MLLR adaptation of continuous density HMM is investigated in a Farsi speaker independent large vocabulary continuous speech recognition system in attempt to improve recognition rate in real world situations. In the MLLR framework, we have experienced the use of Gaussian mean transformations in global adaptation and regression tree based adaptation. Besides full and block-diagonal...

introduction: supplying the requirements and consequently the customer’s satisfaction has been one of the fundamental issues since the last decade. in active organizations in the section of health and treatment, individual’s satisfaction will be considered as their feelings toward realizing their expectations. therefore, the aim of study was to investigate the client needs in the speech therapy...

For single-channel speech enhancement, mask learning based approach through neural network has been shown to outperform the feature mapping approach, and to be effective as a pre-processor for automatic speech recognition. However, its assumption that the mixture and clean reference must have the correspondent scale doesn’t hold in data collected from real world, and thus leads to significant p...

Sequence-to-sequence attentional-based neural network architectures have been shown to provide a powerful model for machine translation and speech recognition. Recently, several works have attempted to extend the models for end-to-end speech translation task. However, the usefulness of these models were only investigated on language pairs with similar syntax and word order (e.g., English-French...

The problem of learning a mapping from strings to strings arises in many areas of text and speech processing. As an example, an important component of speech recognition or speech synthesis systems is a pronunciation model, which provides the possible phonemic transcriptions of a word, or a sequence of words. An accurate pronunciation model is crucial for the overall quality of such systems. An...

This paper presents an implemented computational model of word acquisition which learns directly from raw multimodal sensory input. Set in an information theoretic framework, the model acquires a lexicon by finding and statistically modeling consistent cross-modal structure. The model has been implemented in a system using novel speech processing, computer vision, and machine learning algorithm...

The influence of a native language on learning new speech sounds in adulthood is addressed using a network model in which speech categories are attractors implemented through interactive activation and Hebbian learning. The network has a representation layer that receives topographic projections from an input layer and has reciprocal excitatory connections with deeper layers. When applied to an...

Deep Neural Networks (DNN) have been successful in enhancing noisy speech signals. Enhancement is achieved by learning a nonlinear mapping function from the features of the corrupted speech signal to that of the reference clean speech signal. The quality of predicted features can be improved by providing additional side channel information that is robust to noise, such as visual cues. In this p...