CONVERSATION ENGINE AND RELATED METHODS
A conversation engine includes: an input module for provision of speech data, the speech data including first speech data based on a first speech signal from a first speaker; a sentiment feature extractor for provision of sentiment metric data based on the speech data, the sentiment metric data including first sentiment metric data based on the first speech data; a text feature extractor for provision of text metric data based on the speech data, the text metric data including first text metric data based on the first speech data; a training generator configured to generate a first output sentiment and a first output text based on the first sentiment metric data and/or the first text metric data; and a speech generator configured to output a first output speech signal based on the first output sentiment and the first output text.
1 . A conversation engine comprising:
an input module for provision of speech data, the speech data including first speech data based on a first speech signal from a first speaker;
a sentiment feature extractor for provision of sentiment metric data based on the speech data, the sentiment metric data including first sentiment metric data based on the first speech data;
a text feature extractor for provision of text metric data based on the speech data, the text metric data including first text metric data based on the first speech data;
a training generator configured to generate a first output sentiment and a first output text based on the first sentiment metric data and/or the first text metric data; and
a speech generator configured to output a first output speech signal based on the first output sentiment and the first output text.
2 . The conversation engine according to claim 1 , wherein the training generator comprises a text generator for provision of the first output text, where the first output text is based on the sentiment metric data and/or the first text metric data.
3 . The conversation engine according to claim 1 , wherein the training generator comprises a sentiment generator for provision of the first output sentiment, where the first output sentiment is based on the first sentiment metric data and/or the first text metric data.
4 . The conversation engine according to claim 1 , wherein the first output sentiment comprises a speaking tone parameter, a speaking trait parameter, a vocal trait parameter, or any combination of the foregoing.
5 . The conversation engine according to claim 1 , wherein the first output text comprises a sentence including one or more words, one or more language identifiers, a text theme identifier, a text lexical field identifier, a text definition identifier, or any combination of the foregoing.
6 . The conversation engine according to claim 1 , further comprising a voice activity sensor configured to detect speech.
7 . The conversation engine according to claim 6 , wherein when the voice activity sensor comprises a turn detector, the turn detector being configured to detect a speaker turn.
8 . The conversation engine according to claim 6 , wherein the voice activity sensor is configured to detect a termination of a conversation based on a stop criterion.
9 . The conversation engine according to claim 1 , wherein the first sentiment metric data comprises a first speaking tone parameter, a first speaking trait parameter, a first vocal trait parameter, or any combination of the foregoing.
10 . The conversation engine according to claim 1 , wherein the first text metric data comprises a sentence including one or more words, one or more language identifiers, a text theme identifier, a text lexical field identifier, a text definition identifier, or any combination of the foregoing.
11 . The conversation engine according to claim 1 , wherein the first output sentiment matches at least partly the first sentiment metric data.
12 . The conversation engine according to claim 1 , wherein the first output text matches at least partly the first text metric data.
13 . The conversation engine according to claim 1 , wherein the conversation engine is configured to obtain one or more audio/conversation recordings; and
wherein the training generator is configured to be updated according to the one or more audio/conversation recordings.
14 . The conversation engine according to claim 1 , further comprising a receiver configured to output the first output speech signal.
15 . A computer implemented method, comprising:
obtaining, via an input module, speech data, the speech data including first speech data based on a first speech signal from a first speaker;
determining, using a sentiment feature extractor, sentiment metric data based on the speech data, the sentiment metric data including first sentiment metric data based on the first speech data;
determining, using a text feature extractor, text metric data based on the speech data, the text metric data including first text metric data based on the first speech data;
generating, using a training generator, a first output sentiment and a first output text based on the first sentiment metric data and/or the first text metric data; and
outputting, using a speech generator, a first output speech signal based on the first output sentiment and the first output text.