Speech recognition
Speech recognition technologies allow computers equipped with microphones to interpret human speech, e.g. for transcription or as a control method.Such systems can be classified as to whether
- they require the user to "train" the system to recognise their own particular speech patterns or not,
- whether the system is trained for one user only or is speaker independent,
- whether the system can recognise continuous speech or requires users to break up their speech into discrete words,
- whether the system is intended for clear speech material, or is designed to operate on distorted transfer channels (e.g. cellular phones) and possibly background noise or other speaker talking simultaneously.
- and whether the vocabulary the system recognises is small (in the order of tens or at most hundreds of words), or large (thousands of words).
Commercial systems for speech recognition have been available off-the-shelf since the 1990s. However, it is interesting to note that despite the apparent success of the technology, few people use such speech recognition systems.
It appears that most computer users can create and edit documents more quickly with a conventional keyboard, despite the fact that most people are able to speak considerably faster than they can type. Additionally, heavy use of the speech organs results in vocal loading. Also, the typical office environment with a high amplitude of background speechs are among the most adverse environment for current speech recognition technologies.
Large-vocabulary systems with speaker-independence and/or are designed to operate within an adverse environment, however, have significantly lower recognition rates. The typical achievable recognition rate (2003) for large-vocabulary speaker-indenependent are about 80%-90% for clear environment, and can be as low as 50% for scenarios like cellular phone with background noise.
Some of the key technical problems in speech recognition are that:
- Inter-speaker differences and also intra-speaker variations are often large and difficult to account for. It is not clear which characteristics of speech are speaker-independent.
- Speech recognition system are based on simplified sochastic models, that do not match the real speech accurately.
- The interpretation of many phonemes, words and phrases are context sensitive. For example, phonemes are often shorter in long words than in short words. Words have different meanings in different sentences, e.g. "Philip lies" could be interpreted either as Philip being a liar, or that Philip is lying on a bed.
- Intonation and speech timbre can completely change the correct interpretation of a word or sentence, e.g. "Go!", "Go?" and "Go." can clearly be recognised by a human, but not so easily by a computer.
- Words and sentences can have several valid interpretations such that the speaker leaves the choice of the correct one to the listener.
- Written language may need punctuation according to strict rules that are not strongly present in speech, and are difficult to infer without knowing the meaning (commas, ending of sentences, quotations).
- I helped Apple wreck a nice beach,
A general solution of many of the above problems effectively requires human knowledge and experience, and would thus require advanced pattern recognition and artificial intelligence technologies to be implemented on a computer. In particular, statistical language models are often employed for disambiguation and improvement of the recognition accuracies.