Abstract: When transcribing audio recordings, such as legal depositions, phrases may be repeated throughout the recordings, but these repeated phrases get transcribed incorrectly by an automatic speech recognition (ASR) system. In order to assist a transcriber to correctly resolve such phrases, some embodiments described herein involve a computer that receives an audio recording that includes speech, generates a transcription of the audio recording utilizing an ASR system, and clusters segments of the audio recording into clusters of similar utterances. The computer provides a transcriber with certain segments of the audio recording, which include similar utterances belonging to a certain cluster, along with transcriptions of the certain segments. The computer receives from the transcriber: an indication of which of the certain segments include repetitions of a phrase, and a correct transcription of the phrase.

Abstract: When transcribing audio recordings, such as legal depositions, phrases may be repeated throughout the recordings, but these repeated phrases get transcribed incorrectly by an automatic speech recognition (ASR) system. In order to assist a transcriber to correctly resolve such phrases, some embodiments described herein involve a computer that receives an audio recording that includes speech, generates a transcription of the audio recording utilizing an ASR system, and clusters segments of the audio recording into clusters of similar utterances. The computer provides a transcriber with certain segments of the audio recording, which include similar utterances belonging to a certain cluster, along with transcriptions of the certain segments. The computer receives from the transcriber: an indication of which of the certain segments include repetitions of a phrase, and a correct transcription of the phrase.

Abstract: When transcribing an audio recording, certain phrases may be difficult to resolve, especially if they involve names and/or infrequently used terms. However, often such phrases may be repeated multiple times throughout the audio recording. Embodiments described herein interact with a transcriber to resolve such cases of repeated phrases. In one embodiment, a computer plays segments of an audio recording to the transcriber, and at least some of the segments include an utterance of a phrase. The computer also presents, to the transcriber, transcriptions of the segments, and at least some of the transcriptions do not include a correct transcription of the phrase. The computer receives from the transcriber an indication of which of the segments include an utterance of the phrase and the correct transcription of the phrase, and then updates a transcription of the audio recording accordingly.

Abstract: Maintaining adequate audio quality is very important for creating fast and accurate transcriptions, especially in a hybrid transcription setting, in which human transcribers review transcriptions generated by automatic speech recognition (ASR) systems. Some embodiments described herein involve detecting low-quality audio intended for transcription. In one embodiment, a server receives an audio recording that includes speech. The server generates feature values based on a segment of the audio recording and utilizes a model to calculate, based on the feature values, a certain value indicative of expected hybrid transcription quality of the segment. The model is generated based on training data that includes feature values generated based on previously recorded segments of audio, and values of transcription-quality metrics generated based on transcriptions of the previously recorded segments, which were generated at least in part by human transcribers.

Abstract: Hybrid transcription of audio relies on having one or more layers of transcribers who review transcriptions generated by automatic speech recognition (ASR) systems in order to correct errors that are found in the transcriptions. When it comes to determining how much human reviewing is needed, such as determining how many layers of review to use, there is a cost/benefit tradeoff to consider. Some embodiments described herein utilize a machine learning-based approach for estimating quality of hybrid transcription of audio. In one embodiment, a computer generates a transcription of a segment of audio using an ASR system, which is subsequently reviewed by a transcriber. The computer then calculates, based on properties of the review by the transcriber, a value indicative of an expected accuracy of the reviewed transcription. The computer may suggest a second transcriber review the reviewed transcription if the value indicative of the expected accuracy is below a threshold.

Abstract: When transcribing large audio files, such as in the case of legal depositions, there are often many transcribers to choose from. Embodiments described herein enable calculation of expected accuracy of transcriptions by transcribers, which can be used to guide the selection of transcribers for specific tasks. In one embodiment, a computer receives a segment of an audio recording that includes speech of a person, and identifies an accent of the person and a topic of the segment. The computer generates feature values based on data that includes the accent and the topic, and utilizes a model to calculate, based on the feature values, an expected accuracy of a transcription of the segment by a certain transcriber. The model is generated based on training data that includes segments of previous audio recordings and values of accuracies of transcriptions, by the certain transcriber, of the segments.