Sentence examples for phone alignment from inspiring English sources

Both A1 and A2 require collection of phone hypotheses using word-based phone alignment.

For clarity, we refer to this conventional phone hypothesis approach as "word-based phone alignment", and the generated phone lattices as "word-based phone lattices".

The algorithm is summarized as follows: Step 1. Decode the input speech utterance with an LVCSR decoder, collecting phone back-tracking information using word-based phone alignment to generate a word-based phone lattice.

The algorithm is summarized as follows: Step 1. Decode the input speech utterance with an LVCSR decoder, collecting phone back-tracking information in both the word-based phone alignment and bag-of-phone methods.

As opposed to "word-based phone alignment", the "bag of phones" approach collects phone hypotheses generated at each frame during LVCSR decoding into a phone bag regardless of the word it belongs to.

This is the most common method for phone lattice generation in LVCSR, and the generated phone lattices can be viewed as exact phone alignments of the word lattice.

A magnet ensures the phone is aligned properly; the mat chirps when the alignment is correct, and again when the phone is removed.

The key idea of the time-aligned phone lattice approach is to find the best time alignment in order to remove pronunciation dictionary and LM constraints.

The decision tree-based state clustering tool of HTK produced 858 tied states, and evaluating the phone models in forced alignment mode yielded the training targets for each frame of speech.

Given a phone lattice, the basic alignment procedure is that of finding an ordered link equivalence that is consistent with the lattice ordering and is also a total ordering.

My previously-crippled Android 1.5 phone, the GSM HTC Hero will finally (pending alignment of the stars), receive the bump to Android 2.1 as of Friday, June 4th.