1. A method for automatically transcribing a customer service telephone conversation between an arbitrary number of speakers, the method comprising: receiving data corresponding to the telephone conversation, wherein the received data comprises audio data and metadata that identifies one or more of the speakers in the audio data; separating the audio data into frames; analyzing the frames to identify utterances, wherein each utterance comprises a plurality of frames; performing blind diarization of the audio data to differentiate speakers, wherein the blind diarization comprises: representing each utterance as a utterance model based on acoustic features of each utterance, clustering the utterance models, creating speaker models from each of the clusters, constructing a hidden Markov model from the speaker models, and decoding the hidden Markov model to differentiate speakers of each utterance; tagging homogeneous speaker segments in the telephone conversation with a tag unique for each speaker; performing speaker diarization to replace one or more of the tags with a speaker's identity, wherein the speaker diarization comprises: comparing the homogeneous speaker segments in the telephone conversation to one or more models retrieved from a database wherein the one or more models retrieved correspond to the one or more speakers identified in the metadata, and based on the comparison, identifying one or more of the speakers; and transcribing the conversation to obtain a text representation of the conversation, wherein each spoken part of the conversation is labeled with either the speaker's identity or the tag associated with the speaker.
1. A method for automatically transcribing a customer service telephone conversation between an arbitrary number of speakers, the method comprising: receiving data corresponding to the telephone conversation, wherein the received data comprises audio data and metadata that identifies one or more of the speakers in the audio data; separating the audio data into frames; analyzing the frames to identify utterances, wherein each utterance comprises a plurality of frames; performing blind diarization of the audio data to differentiate speakers, wherein the blind diarization comprises: representing each utterance as a utterance model based on acoustic features of each utterance, clustering the utterance models, creating speaker models from each of the clusters, constructing a hidden Markov model from the speaker models, and decoding the hidden Markov model to differentiate speakers of each utterance; tagging homogeneous speaker segments in the telephone conversation with a tag unique for each speaker; performing speaker diarization to replace one or more of the tags with a speaker's identity, wherein the speaker diarization comprises: comparing the homogeneous speaker segments in the telephone conversation to one or more models retrieved from a database wherein the one or more models retrieved correspond to the one or more speakers identified in the metadata, and based on the comparison, identifying one or more of the speakers; and transcribing the conversation to obtain a text representation of the conversation, wherein each spoken part of the conversation is labeled with either the speaker's identity or the tag associated with the speaker. 8. The method according to claim 1 , wherein the acoustic features are vectors comprised of Mel-frequency cepstral coefficients.