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Showing 1 to 8 of 8 for “"Phoneme recognition"”.

  1. Automatic Phoneme Recognition with Segmental Hidden Markov Models

    A speaker independent continuous speech phoneme recognition and segmentation system is presented. We discuss the training and recognition phases of the phoneme recognition system as well as a detailed description of the integrated elements. The Hidden Markov Model (HMM) based phoneme models are …

    vt Repository record for Automatic Phoneme Recognition with Segmental Hidden Markov Models (opens in a new tab)

  2. Acoustic Modeling and Feature Selection for Speech Recognition

    … formant tracker system is extended to perform phoneme recognition. The results indicate that the incapability of estimating the system measurement error prevents the system from performing well in the phoneme recognition tasks. In the third part, an SVM and HMM combined system is used to prove …

    uiuc Repository record for Acoustic Modeling and Feature Selection for Speech Recognition (opens in a new tab)

  3. Deep neural network acoustic models for multi-dialect Arabic speech recognition

    … The major concerns of the automatic speech recognition (ASR) are determining a set of classification features and finding a suitable recognition model for these features. Hidden Markov Models (HMMs) have been demonstrated to be powerful models for representing time varying signals. …

    nott-trent Repository record for Deep neural network acoustic models for multi-dialect Arabic speech recognition (opens in a new tab)

  4. Spontaneous speech recognition using HMMs

    This thesis describes a speech recognition system that was built to support spontaneous speech understanding. The system is composed of (1) a front end acoustic analyzer which computes Mel-frequency cepstral coefficients, (2) acoustic models of context-dependent phonemes (triphones), (3) a back-off …

    mit Repository record for Spontaneous speech recognition using HMMs (opens in a new tab)

  5. Acoustic models for speech recognition using Deep Neural Networks based on approximate math

    … arithmetic and evaluate it on the TIMIT phoneme recognition task and the WSJ speech recognition task. For both tasks, we nd that acoustic models based on approximate DNNs perform as well as ones based on conventional DNNs; both produce similar recognition error rates. The approximate DNN …

    mit Repository record for Acoustic models for speech recognition using Deep Neural Networks based on approximate math (opens in a new tab)

  6. Acoustic Approaches to Gender and Accent Identification

    … research on the problems of speaker and language recognition from samples of speech. A less researched problem is that of accent recognition. Although this is a similar problem to language identification, di�erent accents of a language exhibit more fine-grained di�erences between classes than …

    east-anglia Repository record for Acoustic Approaches to Gender and Accent Identification (opens in a new tab)

  7. An RBFN-based system for speaker-independent speech recognition

    … menu systems. The design of a cascade of recognition layers is presented. Several feature sets are compared. Phone recognition is performed using a radial basis function network (RBFN). Dynamic time warping (DTW) is used for word recognition. The TIMIT database is used to design and test …

    vt Repository record for An RBFN-based system for speaker-independent speech recognition (opens in a new tab)

  8. Perception of prosody by cochlear implant recipients

    … (CIs) display remarkable success with speech recognition in quiet, but not with speech recognition in noise. Normal-hearing (NH) listeners, in contrast, perform relatively well with speech recognition in noise. Understanding which speech features support successful perception in noise in NH …

    pretoria Repository record for Perception of prosody by cochlear implant recipients (opens in a new tab)