Image processing is not a common approach to speech recognition, as it deals with the analysis of visual information rather than audio signals.

Acoustic modeling aims to extract meaningful features from speech signals that can be used to represent the underlying speech content.

Mel-frequency cepstral coefficients (MFCCs) are a popular feature extraction technique in speech recognition, as they capture the spectral characteristics of speech signals in a perceptually meaningful way.

Language modeling aims to estimate the probability of a given word sequence, which helps in disambiguating words and phrases during speech recognition.

N-gram language models are commonly used in speech recognition, as they estimate the probability of a word sequence based on the frequency of occurrence of n-grams (sequences of n consecutive words) in a training corpus.

The decoder in speech recognition aims to predict the sequence of words spoken based on the acoustic features and the language model.

Beam search is a widely used decoding algorithm in speech recognition, as it efficiently explores the space of possible word sequences while maintaining a manageable number of hypotheses.

Speaker adaptation aims to improve the accuracy of speech recognition for a specific speaker by adapting the acoustic and/or language models to the speaker's unique vocal characteristics and speaking style.

Maximum likelihood linear regression (MLLR) is a widely used technique for speaker adaptation in speech recognition, as it efficiently adapts the acoustic model parameters to the speaker's vocal characteristics.

Noise reduction aims to improve the accuracy of speech recognition in noisy environments by suppressing background noise and enhancing the speech signal.

Spectral subtraction is a widely used noise reduction technique in speech recognition, as it effectively reduces background noise by subtracting an estimate of the noise spectrum from the speech spectrum.

Reverberation suppression aims to improve the accuracy of speech recognition in reverberant environments by reducing the effects of reverberation, which can distort the speech signal and make it difficult to recognize.

Dereverberation is a widely used reverberation suppression technique in speech recognition, as it effectively reduces the effects of reverberation by estimating and removing the reverberant components from the speech signal.

End-to-end speech recognition aims to eliminate the need for separate acoustic and language models by directly mapping the speech signal to the sequence of words spoken, using a single neural network model.

Attention-based encoder-decoder models are widely used for end-to-end speech recognition, as they effectively capture the temporal dependencies in speech signals and allow for direct mapping between the speech signal and the sequence of words spoken.