Your mileage will vary depending on the playback speakers and microphone available. Depending on the clarity of the recording and the noise in the environment, it is sometimes possible to play the recording into the microphone and be successful in creating an accurate transcript. We've found that this is often faster than listening and typing by hand. One of the most easily implemented and effective ways to speed up transcription work is to make use of automated-typing software like Dragon Naturally Speaking or ViaVoice in a workflow where the operator listens to the audio file and repeats the text into the voice-recognition software. Between the large-scale proprietary tools and developing your own speech-recognition software to transcribe audio files lies the middle ground of improving the performance and productivity of human transcriptionists. Microsoft's Speech API (SAPI) and software development kit (SDK) can be used to build speech-recognition applications in. And there are large commercial systems aimed at providing audio-mining capabilities as part of enterprise search systems available from places like DocSoft. The Speech at CMU project page provides information about open source speech-recognition resources. There are shareware and commercial WAV-to-text programs available for converting audio files to text. Getting accurate transcripts from audio files through software can be tough, but it is getting easier. Are there any affordable, automatic-transcription software tools available or are we stuck using human transcriptionists? We have a large number of audio files we need to make transcripts from.
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