Method of speech recognition using multimodal variational inference with switching state space models

A method of decoding a speech signal to identify a sequence of phonetic units, the method comprising:

• storing model parameters for a switching state space model in which there are discrete hidden states and continuous hidden states, the continuous hidden states being dependent on the discrete hidden states,
• converting the speech signal into a set of observation vectors, each observation vector associated with a separate frame of the speech signal;
• for each frame of the speech signal:
• determining a posterior probability mean for each discrete hidden state, the posterior probability mean defining a mean value for a continuos hidden state given a discrete hidden state and an observation vector wherein determining a posterior probability mean comprises defining a window of frames that contains fewer than all of the frames of the speech signal and determining a separate posterior probability mean for each discrete hidden state in each frame in the window by solving a set of simultaneous equations;
• and
• determining a path score for at least one path into each discrete hidden state in the frame based on the posterior probability mean for the respective discrete hidden state;
• and
• using the path score to select a single path into each discrete hidden state of the frame.