Abstract: A speech synthesis process can record concatenation costs of unit sequential pairs to a concatenation cost database for speech synthesis by synthesizing speech from a text, identifying an acoustic unit sequential pair in the speech, searching for a concatenation cost for the acoustic unit sequential pair in a database using a hash table for the database, and when the concatenation cost is not found in the database, assigning a default value as the concatenation cost for the acoustic unit sequential pair.

Abstract: A speech synthesis process can record concatenation costs of unit sequential pairs to a concatenation cost database for speech synthesis by synthesizing speech from a text, identifying an acoustic unit sequential pair in the speech, searching for a concatenation cost for the acoustic unit sequential pair in a database using a hash table for the database, and when the concatenation cost is not found in the database, assigning a default value as the concatenation cost for the acoustic unit sequential pair.

Abstract: A speech synthesis can record concatenation costs of most common acoustic unit sequential pairs to a concatenation cost database for speech synthesis by synthesizing speech from a text, identifying a most common acoustic unit sequential pair in the speech, assigning a concatenation cost to the most common acoustic sequential pair, and recording the concatenation cost of the most common acoustic sequential pair to a concatenation cost database.

Abstract: A speech synthesis system can record concatenation costs of most common acoustic unit sequential pairs to a concatenation cost database for speech synthesis by synthesizing speech from a text, identifying a most common acoustic unit sequential pair in the speech, assigning a concatenation cost to the most common acoustic sequential pair, and recording the concatenation cost of the most common acoustic sequential pair to a concatenation cost database.

Abstract: A statistical model may be created that relates human ratings of documents to objective signals generated from the documents, search queries, and/or other information (e.g., query logs). The model can then be used to predict human ratings/rankings for new documents/search query pairs. These predicted ratings can be used to, for example, refine rankings from a search engine or assist in evaluating or monitoring the efficacy of a search engine system.

Abstract: Systems and methods for identifying music in a noisy environment are described. One of the methods includes receiving audio segment data. The audio segment data is generated from the portion that is captured in the noisy environment. The method further includes generating feature vectors from the audio segment data, identifying phonemes from the feature vectors, and comparing the identified phonemes with pre-assigned phoneme sequences. Each pre-assigned phoneme sequence identifies a known music piece. The method further includes determining an identity of the music based on the comparison.

Abstract: A speech synthesis system can select recorded speech fragments, or acoustic units, from a very large database of acoustic units to produce artificial speech. When a pair of acoustic units in the database does not have an associated concatenation cost, the system assigns a default concatenation cost. The system then synthesizes speech, identifies the acoustic unit sequential pairs generated and their respective concatenation costs, and stores those concatenation costs likely to occur.

Abstract: A speech synthesis system can select recorded speech fragments, or acoustic units, from a very large database of acoustic units to produce artificial speech. When a pair of acoustic units in the database does not have an associated concatenation cost, the system assigns a default concatenation cost. The system then synthesizes speech, identifies the acoustic unit sequential pairs generated and their respective concatenation costs, and stores those concatenation costs likely to occur.

Abstract: Systems and methods for identifying the N-best strings of a weighted automaton. A potential for each state of an input automaton to a set of destination states of the input automaton is first determined. Then, the N-best paths are found in the result of an on-the-fly determinization of the input automaton. Only the portion of the input automaton needed to identify the N-best paths is determinized. As the input automaton is determinized, a potential for each new state of the partially determinized automaton is determined and is used in identifying the N-best paths of the determinized automaton, which correspond exactly to the N-best strings of the input automaton.

Abstract: A system identifies a document that includes an address and locates business information in the document. The system assigns a confidence score to the business information, where the confidence score relates to a probability that the business information is associated with the address. The system determines whether to associate the business information with the address based on the assigned confidence score.

Abstract: Systems and methods for identifying the N-best strings of a weighted automaton. A potential for each state of an input automaton to a set of destination states of the input automaton is first determined. Then, the N-best paths are found in the result of an on-the-fly determinization of the input automaton. Only the portion of the input automaton needed to identify the N-best paths is determinized. As the input automaton is determinized, a potential for each new state of the partially determinized automaton is determined and is used in identifying the N-best paths of the determinized automaton, which correspond exactly to the N-best strings of the input automaton.

Abstract: A speech synthesis system can select recorded speech fragments, or acoustic units, from a very large database of acoustic units to produce artificial speech. The selected acoustic units are chosen to minimize a combination of target and concatenation costs for a given sentence. However, as concatenation costs, which are measures of the mismatch between sequential pairs of acoustic units, are expensive to compute, processing can be greatly reduced by pre-computing and caching the concatenation costs. Unfortunately, the number of possible sequential pairs of acoustic units makes such caching prohibitive. However, statistical experiments reveal that while about 85% of the acoustic units are typically used in common speech, less than 1% of the possible sequential pairs of acoustic units occur in practice.

Abstract: Systems and methods for identifying the N-best strings of a weighted automaton. A potential for each state of an input automaton to a set of destination states of the input automaton is first determined. Then, the N-best paths are found in the result of an on-the-fly determinization of the input automaton. Only the portion of the input automaton needed to identify the N-best paths is determinized. As the input automaton is determinized, a potential for each new state of the partially determinized automaton is determined and is used in identifying the N-best paths of the determinized automaton, which correspond exactly to the N-best strings of the input automaton.

Abstract: A statistical model may be created that relates human ratings of documents to objective signals generated from the documents, search queries, and/or other information (e.g., query logs). The model can then be used to predict human ratings/rankings for new documents/search query pairs. These predicted ratings can be used to, for example, refine rankings from a search engine or assist in evaluating or monitoring the efficacy of a search engine system.

Abstract: A speech synthesis system can select recorded speech fragments, or acoustic units, from a very large database of acoustic units to produce artificial speech. The selected acoustic units are chosen to minimize a combination of target and concatenation costs for a given sentence. However, as concatenation costs, which are measures of the mismatch between sequential pairs or acoustic units, are expensive to compute, processing can be greatly reduced by pre-computing and aching the concatenation costs. The number of possible sequential pairs of acoustic units makes such caching prohibitive. Statistical experiments reveal that while about 85% of the acoustic units are typically used in common speech, less than 1% of the possible sequential pairs or acoustic units occur in practice. The system synthesizes a large body of speech, identifies the acoustic unit sequential pairs generated and their respective concatenation costs, and stores those concatenation costs likely to occur.

Abstract: A speech synthesis system can select recorded speech fragments, or acoustic units, from a very large database of acoustic units to produce artificial speech. The selected acoustic units are chosen to minimize a combination of target and concatenation costs for a given sentence. However, as concatenation costs, which are measures of the mismatch between sequential pairs of acoustic units, are expensive to compute, processing can be greatly reduced by pre-computing and caching the concatenation costs. Unfortunately, the number of possible sequential pairs of acoustic units makes such caching prohibitive. A method for constructing an efficient concatenation cost database is provided by synthesizing a large body of speech, identifying the acoustic unit sequential pairs generated and their respective concatenation costs. By constructing a concatenation cost database in this fashion, the processing power required at run-time is greatly reduced with negligible effect on speech quality.

Abstract: Systems and methods for low-latency real-time speech recognition/transcription. A discriminative feature extraction, such as a heteroscedastic discriminant analysis transform, in combination with a maximum likelihood linear transform is applied during front-end processing of a digital speech signal. The extracted features reduce the word error rate. A discriminative acoustic model is applied by generating state-level lattices using Maximum Mutual Information Estimation. Recognition networks of language models are replaced by their closure. Latency is reduced by eliminating segmentation such that a number of words/sentences can be recognized as a single utterance. Latency is further reduced by performing front-end normalization in a causal fashion.

Abstract: Systems and methods for low-latency real-time speech recognition/transcription. A discriminative feature extraction, such as a heteroscedastic discriminant analysis transform, in combination with a maximum likelihood linear transform is applied during front-end processing of a digital speech signal. The extracted features reduce the word error rate. A discriminative acoustic model is applied by generating state-level lattices using Maximum Mutual Information Estimation. Recognition networks of language models are replaced by their closure. Latency is reduced by eliminating segmentation such that a number of words/sentences can be recognized as a single utterance. Latency is further reduced by performing front-end normalization in a causal fashion.

Abstract: A system identifies a document that includes an address and locates business information in the document. The system assigns a confidence score to the business information, where the confidence score relates to a probability that the business information is associated with the address. The system determines whether to associate the business information with the address based on the assigned confidence score.

Abstract: A speech synthesis system can select recorded speech fragments, or acoustic units, from a very large database of acoustic units to produce artificial speech. The selected acoustic units are chosen to minimize a combination of target and concatenation costs for a given sentence. However, as concatenation costs, which are measures of the mismatch between sequential pairs of acoustic units, are expensive to compute, processing can be greatly reduced by pre-computing and aching the concatenation costs. Unfortunately, the number of possible sequential pairs of acoustic units makes such caching prohibitive. However, statistical experiments reveal that while about 85% of the acoustic units are typically used in common speech, less than 1% of the possible sequential pairs of acoustic units occur in practice.