amazon voice recognition software adrino

The weights depend on correlations between the frequency characteristics of the frames the greater the correlation, the greater the weight. For a given utterance, Li and his colleagues’ model represents each frame as a weighted sum of itself and all the other frames in the utterance. Speech signals are typically divided into frames, which represent power concentrations at different sound frequencies over short spans of time. In neural networks - such as speech processors - that receive sequential inputs, attention mechanisms determine which other elements of the sequence should influence the network’s judgment about the current element. In “ Speaker identification for household scenarios with self-attention and adversarial training”, Amazon applied scientist Ruirui Li and colleagues at Amazon, the University of California, Los Angeles, and the University of Notre Dame instead use an attention mechanism to identify longer-range consistencies in the speech signal. Speaker ID systems - which enable voice agents to personalize content to particular customers - typically rely on either recurrent neural networks or convolutional neural networks, both of which are able to track consistencies in the speech signal over short spans of time.

In his keynote address this week at Interspeech, Alexa director of ASR Shehzad Mavawalla highlighted both of these areas - speaker ID and the use of RNN-Ts for ASR - as ones in which the Alexa science team has made rapid strides in recent years. Two others - “ Subword regularization: an analysis of scalability and generalization for end-to-end automatic speech recognition” and “ Efficient minimum word error rate training of RNN-transducer for end-to-end speech recognition” -examine ways to improve the quality of speech recognizers that use an architecture know as a recurrent neural network-transducer, or RNN-T. One of these, “ Speaker identification for household scenarios with self-attention and adversarial training”, reports the speech team’s recent innovations in speaker ID, or recognizing which of several possible speakers is speaking at a given time. From 'Efficient minimum word error rate training of RNN-transducer for end-to-end speech recognition'

Amazon voice recognition software adrino