Multi-frame prediction for hybrid neural network/hidden Markov models

A system comprising:

• one or more processors;
• memory;
• and
• machine-readable instructions stored in the memory, that upon execution by the one or more processors cause the system to carry out operations comprising:
• transforming an audio input signal into a first time sequence of feature vectors, wherein each respective feature vector of the first time sequence bears quantitative measures of acoustic properties of a corresponding, respective temporal frame of a first sequence of temporal frames of the audio input signal,
• providing, at a first time step, the first time sequence of feature vectors as input to a neural network (NN) implemented by the system,
• by concurrently processing the feature vectors in the first time sequence with the NN during the first time step, concurrently determining emission probabilities corresponding to all the temporal frames of the first sequence of temporal frames, wherein concurrently determining the emission probabilities corresponding to all the temporal frames of the first sequence of temporal frames comprises:
• during a common time interval, determining for each feature vector in the first time sequence a respective set of emission probabilities for a first plurality of hidden Markov models (HMMs), and
• concurrently applying the emission probabilities determined at the first time step for the feature vectors in the first time sequence to the first plurality of HMMs to determine speech content corresponding to the first sequence of temporal frames of the audio input signal.

An article of manufacture including a non-transitory, computer-readable storage medium, haying stored thereon program instructions that, upon execution by one or more processors of a system, cause the system to perform operations comprising:

• transforming an audio input signal into a first time sequence of feature vectors, wherein each respective feature vector of the first time sequence bears quantitative measures of acoustic properties of a corresponding, respective temporal frame of a first sequence of temporal frames of the audio input signal;
• providing, at a first time step, the first time sequence of feature vectors as input to a neural network (NN) implemented by the system;
• by concurrently processing the feature vectors in the first time sequence with the NN during the first time step, concurrently determining emission probabilities corresponding to all the temporal frames of the first sequence of temporal frames, wherein concurrently determining the emission probabilities corresponding to all the temporal frames of the first sequence of temporal frames comprises:
• during a common time interval, determining for each feature vector in the first time sequence a respective set of emission probabilities for a first plurality of hidden Markov models (HMMs);
• and
• concurrently applying the emission probabilities determined at the first time step for the feature vectors in the first time sequence to the first plurality of HMMs to determine speech content corresponding to the first sequence of temporal frames of the audio input signal.