Automatic spoken language identification based on phoneme sequence patterns

A method to identify spoken words in a human language with a language identification engine, comprising:

• receiving an audio stream that includes a spoken language of at least one or more unidentified human languages being spoken in the audio stream under analysis;
• identifying a most likely phoneme occurring each time in the audio stream under analysis with a universal phoneme decoder that 1) contains a universal phoneme set representing phonemes occurring in a set of two or more spoken languages, and 2) captures phoneme correspondences across languages, such that a set of unique phoneme patterns and probabilities are calculated;
• generating as an output from the universal phoneme decoder one or more streams of identified phonemes with associated confidence ratings on an accuracy of the identification, where a first stream of identified phonemes is customized to at least one of 1) a particular spoken human language and 2) a specific dialect of a spoken human language, and the first stream contains one or more estimations for identifying the spoken phonemes in that particular spoken human language or specific dialect along with a confidence rating, where a second stream of identified phonemes is customized to at least one of 1) a particular spoken human language and 2) a specific dialect of a spoken human language, and the spoken human language or dialect chosen for the second stream is different than the first stream;
• and
• at run-time, identifying a most likely particular human language being spoken in the received audio stream in the one or more streams of phonemes outputted from the universal phoneme decoder by utilizing linguistic probabilities supplied by one or more statistical language models that are based on the set of unique phoneme patterns created for each language by the universal phoneme decoder.

A system including a continuous speech recognition engine hosted on a server that cooperates with a language identification engine in order to improve an accuracy of probability estimates, comprising:

• an input to receive supplied audio files from a client machine over a wide area network to the server hosting the continuous speech recognition engine;
• and
• wherein the language identification engine at least includes
• a front end module having an input configured to receive the supplied audio files that include a spoken language of at least one of a set of two or more candidate languages being spoken in the supplied audio files under analysis,
• a universal phoneme decoder that 1) contains a universal phoneme set representing the phonemes occurring in the set of two or more spoken languages, and 2) captures phoneme correspondences between languages, such that a set of unique phoneme patterns and probabilities are calculated in order to identify a most likely phoneme occurring each time in the audio files in the set of two or more candidate languages in which the universal phoneme decoder was trained on,
• one or more statistical language models having logic configured to supply to a run-time language identifier module probabilities of how linguistically likely a particular uttered phoneme identified by the universal phoneme decoder comes from a particular spoken language based on an identified sequence of phonemes, and
• where the run-time language identifier module is configured to identify from the set of two or more candidate languages at least one of 1) a particular spoken human language and 2) a specific dialect of a spoken human language being spoken in the supplied audio files by utilizing the linguistic probabilities supplied by the one or more statistical language models that are based on the set of unique phoneme patterns created for each language by the universal phoneme decoder, wherein the modules making up the language identification engine are implemented in electronic circuits, software coding, and any combination of the two, where portions implemented in software coding are stored in an executable format by a processor on a non-transitory machine-readable medium.