Towards Deep Object Detection Techniques for Phoneme Recognition

Phoneme Recognition Using Deep Neural Networks

Deep Triphone Embedding Improves Phoneme Recognition

In this paper, we present a novel Deep Triphone Embedding (DTE) representation derived from Deep Neural Network (DNN) to encapsulate the discriminative information present in the adjoining speech frames. DTEs are generated using a four hidden layer DNN with 3000 nodes in each hidden layer at the first-stage. This DNN is trained with the tied-triphone classification accuracy as an optimization c...

Allophone-based acoustic modeling for Persian phoneme recognition

Phoneme recognition is one of the fundamental phases of automatic speech recognition. Coarticulation which refers to the integration of sounds, is one of the important obstacles in phoneme recognition. In other words, each phone is influenced and changed by the characteristics of its neighbor phones, and coarticulation is responsible for most of these changes. The idea of modeling the effects o...

Phoneme Boundary Detection using Deep Bidirectional LSTMs

In this paper we investigate the automatic detection of phoneme boundaries in audio recordings with the help of deep bidirectional LSTMs. This work is motivated by the needs of the project BULB which aims to support linguists in documenting unwritten languages. The automatic detection of phoneme boundaries in audio recordings of a new language is part of the technical requirements of the BULB p...

Towards Lower Error Rates in Phoneme Recognition

We investigate techniques for acoustic modeling in automatic recognition of context-independent phoneme strings from the TIMIT database. The baseline phoneme recognizer is based on TempoRAl Patterns (TRAP). This recognizer is simplified to shorten processing times and reduce computational requirements. More states per phoneme and bi-gram language models are incorporated into the system and eval...