A phonetically transparent technique for the automatic transcription of speech
This thesis describes a technique for automatic transcription of speech that is both probabilistic and phonetically transparent. The technique employs classical methods of statistical pattern-recognition, but works within a phonetic framework that is simple to conceptualise, with no ~black box~ components or hidden states or layers. The technique being statistical, it involves a prior training-phase during which the features of the classes to be recognised are learnt from examples, and statistical models estimated for each class. These models subsequently become the basis of the recognition procedure. In the present implementation, the classes are classes of ~sub-phonic~ entities (where ~sub-phonic~ denotes parts of phones and phones are conceived as artificially isolated sections of the acoustic record of speech, each phone serving an identifiable linguistic function). The sub-phonic entities (or 'subphones') are defined in such a way as to take account of the underlying articulatory reality, and the manner of their definition imposes a significant amount of constraint on the search for the most probable utterance-transcription, a search executed using dynamic programming. The subphones are identified explicitly
Morony, Michael J.
A phonetically transparent technique for the automatic transcription of speech. This thesis describes a technique for automatic transcription of speech that is both probabilistic and phonetically transparent. The technique employs classical methods of statistical pattern-recognition, but works within a phonetic framework that is simple to conceptualise, with no ~black box~ components or hidden states or layers. The technique being statistical, it involves a prior training-phase during which the features of the classes to be recognised are learnt from examples, and statistical models estimated for each class. These models subsequently become the basis of the recognition procedure. In the present implementation, the classes are classes of ~sub-phonic~ entities (where ~sub-phonic~ denotes parts of phones and phones are conceived as artificially isolated sections of the acoustic record of speech, each phone serving an identifiable linguistic function). The sub-phonic entities (or 'subphones') are defined in such a way as to take account of the underlying articulatory reality, and the manner of their definition imposes a significant amount of constraint on the search for the most probable utterance-transcription, a search executed using dynamic programming. The subphones are identified explicitly
phonetically transparent technique automatic transcription speech describes technique automatic transcription speech probabilistic phonetically transparent technique_employ classical method statistical pattern recognition work within phonetic framework simple conceptualise black box component hidden state layer technique statistical involve prior training phase feature class recognise learnt example statistical_model estimate class model subsequently become basis recognition procedure present implementation class class sub entity sub part phone phone conceive artificially isolated section acoustic record speech phone serve identifiable linguistic function sub entity define way take_account underlie articulatory reality manner definition imposes significant_amount constraint search probable utterance transcription search execute use dynamic programming identify explicitly