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# Speech-to-Text

HPE IDOL Speech Server’s approach to performing speech-to-text is motivated by an information breakdown point of view of speech. This state-of-the-art approach is used by most leading speech technologists.

- Sentences are composed of words (the basis for the language models).
- Words are composed of phonemes and allophones (for detailed definitions of both terms, see http://www.sil.org/linguistics/glossaryOfLinguisticTerms). This is the basis for the pronunciation dictionaries.
- Each phoneme and allophone is described in terms of frequency spectrum-based features (the basis for the acoustic models).
- Signal processing analysis (performed by the front-end algorithms) converts an audio signal into frequency spectrum-based features.

This approach means that the speech-to-text engine requires a language pack that contains:

- A language model that contains information about how sentences are composed of words, as well as the word pronunciation dictionary
- An acoustic model that describes feature patterns for a complete set of allophones used by the particular natural language.

The following diagram shows the inputs and resources that the speech-to-text engine receives.

| Models of fundamental sound patterns Telephone models: 8 kHz Broadcast models: 16 kHz or above
| Pronunciation dictionary with vocabulary | Base language models and customized models

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