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Phone recognition is an important step in integrating spoken language processing into Sigma.
Improving Language Recognition with Multilingual Phone Recognition and Speaker Adaptation Transforms.
Phone Recognition for Mixed Speech Signals: Comparison of Human Auditory Cortex and Machine Performance.
All of the knowledge used in phone recognition was added supraarchitecturally – i.e. on top of the architecture – without requiring the addition of new mechanisms to the architecture.
The structure and training of this recurrent connection, is designed in such a way that the network learns to denoise the signal step by step, using properties of attractors it has formed, along with phone recognition.
phone recognition - vector space modeling.
parallel phone recognition - vector space modeling.
Since the goal is phone recognition, the question remains as to how can these clusters help to improve phone recognition.
All phone recognition tests were performed according to various values of hyperparameter τ.
Experiments show improvements in phone recognition on the TIMIT database compared to a baseline system.
Figure 2 (a) Confusion matrix of our TIMIT-based phone recognition system.
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