Speech recognition for medical conversations
Table 2: Analyzing CTC results
['Category', 'Segments', 'Words', 'WER Unidi', 'WER Bidi']
We trained both unidirectional and bidirectional CTC models. For the unidirectional model we add an output delay of 10 steps. The model has 5 LSTM layers with 700 units per layer, and reached a WER of 28.8% after CTC training criteria and improved to 23.5% with EMBR training. Training a model with 5 bidirectional LSTM layers and 700 units per layer (350 units for either direction) gives 25.2% WER that improves to 20.1% after EMBR training. Most of the errors seem to appear at the beginning and end of the utterances, e.g.: “that’s all you needed” becomes “ that’s all you need it”. Anecdotally it was observed that when the speakers are talking over each other, the CTC model deletes words in that region of audio. A major portion of errors is accounted by small speaker turn segments (<1sec long), for longer utterances model misses out on conversational part of the speech e.g. : “uh i am” transcribed as “um i mean”. In general the WER on patient speaker turns is worse than doctors, this is possibly because the recording device was placed closer to the doctors and hence doctors’ speech is much clearer than the patient.