METHOD AND APPARATUS FOR DETERMINING SPEAKER EFFECTIVENESS IN CONVERSATIONS
In a method and an apparatus for determining speaker effectiveness in conversations, the method includes determining a sentiment transition (ST) score in a consecutive speaker turn pair in a conversation between a first speaker and a second speaker. The ST score measures whether the sentiment transition from the first speaker to the second speaker is negative, neutral, or positive. The method further includes determining a semantic classification (SC) score in the speaker turn pair. The SC score measures the relevance of utterances of the second speaker to the utterance of the first speaker. The method further includes determining an empathy score for the second speaker in the speaker turn pair based on the ST score and the SC score.
1 . A computer implemented method for determining speaker effectiveness in conversations, the method comprising:
determining, at an analytics server, a sentiment transition (ST) score in a consecutive speaker turn pair in a conversation between a first speaker and a second speaker, wherein the ST score measures whether the sentiment transition from the first speaker to the second speaker is negative, neutral, or positive;
determining, at the analytics server, a semantic classification (SC) score in the speaker turn pair, wherein the SC score measures the relevance of utterances of the second speaker to the utterance of the first speaker; and
determining, at the analytics server, an empathy score for the second speaker in the speaker turn pair based on the ST score and the SC score.
2 . The computer implemented method of claim 1 , wherein the empathy score is determined to be high if the ST score is neutral or high, and the SC score is high.
3 . The computer implemented method of claim 2 , wherein the empathy score is determined to be neutral if the ST score is neutral or positive and the SC score is neutral, or if the ST score is negative and the SC score is positive.
4 . The computer implemented method of claim 3 , wherein the empathy score is determined to be negative if the empathy score is neither positive nor neutral.
5 . The computer implemented method of claim 1 , wherein the sentiment for at least one of the first speaker or the second speaker is determined based on at least one of: a transcript, tonal data, or video data of the utterance of the respective speaker.
6 . The computer implemented method of claim 1 , wherein determining at least one of: the sentiment, the ST score, the SC score, or the empathy score using an Artificial Intelligence and/or Machine Learning (AI/ML) model.
7 . A computing apparatus comprising:
a processor; and
a memory storing instructions that, when executed by the processor, configure the apparatus to:
determine, at an analytics server, a sentiment transition (ST) score in a consecutive speaker turn pair in a conversation between a first speaker and a second speaker, wherein the ST score measures whether the sentiment transition from the first speaker to the second speaker is negative, neutral, or positive;
determine, at the analytics server, a semantic classification (SC) score in the speaker turn pair, wherein the SC score measures the relevance of utterances of the second speaker to the utterance of the first speaker; and
determine, at the analytics server, an empathy score for the second speaker in the speaker turn pair based on the ST score and the SC score.
8 . The computing apparatus of claim 7 , wherein the empathy score is determined to be high if the ST score is neutral or high, and the SC score is high.
9 . The computing apparatus of claim 8 , wherein the empathy score is determined to be neutral if the ST score is neutral or positive and the SC score is neutral, or if the ST score is negative and the SC score is positive.
10 . The computing apparatus of claim 9 , wherein the empathy score is determined to be negative if the empathy score is neither positive nor neutral.
11 . The computing apparatus of claim 7 , wherein the sentiment for at least one of the first speaker or the second speaker is determined based on at least one of: a transcript, a tonal data or a video data of the utterance of the respective speaker.
12 . The computing apparatus of claim 7 , wherein at least one of: the sentiment, the ST score, the SC score, or the empathy score is determined using an Artificial Intelligence and/or Machine Learning (AI/ML) model.
13 . A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to:
determine, at an analytics server, a sentiment transition (ST) score in a consecutive speaker turn pair in a conversation between a first speaker and a second speaker, wherein the ST score measures whether the sentiment transition from the first speaker to the second speaker is negative, neutral, or positive;
determine, at the analytics server, a semantic classification (SC) score in the speaker turn pair, wherein the SC score measures the relevance of utterances of the second speaker to the utterance of the first speaker; and
determine, at the analytics server, an empathy score for the second speaker in the speaker turn pair based on the ST score and the SC score.
14 . The computer-readable storage medium of claim 13 , wherein the empathy score is determined to be high if the ST score is neutral or high, and the SC score is high, wherein the empathy score is determined to be neutral if the ST score is neutral or positive and the SC score is neutral, or if the ST score is negative and the SC score is positive, and wherein the empathy score is determined to be negative if the empathy score is neither positive nor neutral.
15 . The computer-readable storage medium of claim 13 , wherein the sentiment for at least one of the first speaker or the second speaker is determined based on at least one of: a transcript, tonal data, or video data of the utterance of the respective speaker.
16 . The computer-readable storage medium of claim 13 , wherein determining at least one of: the sentiment, the ST score, the SC score, or the empathy score using an Artificial Intelligence and/or Machine Learning (AI/ML) model.