Abstract: Aspects of this disclosure are directed to techniques that enable efficient automated text-to-speech pronunciation editing for long form text documents. A computing device comprising a memory and a processor may be configured to perform the techniques. The memory may store a text document. The processor may process words in the text document to identify first candidate words that are predicted to be mispronounced during automated text-to-speech processing of the text document. The processor may next filter the first candidate words to remove one or more candidate words of the first candidate words and obtain second candidate words that have fewer candidate words than the first candidate words. The processor may then annotate the text document to obtain an annotated text document that identifies the second candidate words, and output at least a portion of the annotated text document that identifies at least one candidate word of the second candidate words.

Abstract: A method for estimating an embedding capacity includes receiving, at a deterministic reference encoder, a reference audio signal, and determining a reference embedding corresponding to the reference audio signal, the reference embedding having a corresponding embedding dimensionality. The method also includes measuring a first reconstruction loss as a function of the corresponding embedding dimensionality of the reference embedding and obtaining a variational embedding from a variational posterior. The variational embedding has a corresponding embedding dimensionality and a specified capacity. The method also includes measuring a second reconstruction loss as a function of the corresponding embedding dimensionality of the variational embedding and estimating a capacity of the reference embedding by comparing the first measured reconstruction loss for the reference embedding relative to the second measured reconstruction loss for the variational embedding having the specified capacity.

Abstract: A method for estimating an embedding capacity includes receiving, at a deterministic reference encoder, a reference audio signal, and determining a reference embedding corresponding to the reference audio signal, the reference embedding having a corresponding embedding dimensionality. The method also includes measuring a first reconstruction loss as a function of the corresponding embedding dimensionality of the reference embedding and obtaining a variational embedding from a variational posterior. The variational embedding has a corresponding embedding dimensionality and a specified capacity. The method also includes measuring a second reconstruction loss as a function of the corresponding embedding dimensionality of the variational embedding and estimating a capacity of the reference embedding by comparing the first measured reconstruction loss for the reference embedding relative to the second measured reconstruction loss for the variational embedding having the specified capacity.

Abstract: A system for generating an output audio signal includes a context encoder, a text-prediction network, and a text-to-speech (TTS) model. The context encoder is configured to receive one or more context features associated with current input text and process the one or more context features to generate a context embedding associated with the current input text. The text-prediction network is configured to process the current input text and the context embedding to predict, as output, a style embedding for the current input text. The style embedding specifies a specific prosody and/or style for synthesizing the current input text into expressive speech. The TTS model is configured to process the current input text and the style embedding to generate an output audio signal of expressive speech of the current input text. The output audio signal has the specific prosody and/or style specified by the style embedding.

Abstract: A method for estimating an embedding capacity includes receiving, at a deterministic reference encoder, a reference audio signal, and determining a reference embedding corresponding to the reference audio signal, the reference embedding having a corresponding embedding dimensionality. The method also includes measuring a first reconstruction loss as a function of the corresponding embedding dimensionality of the reference embedding and obtaining a variational embedding from a variational posterior. The variational embedding has a corresponding embedding dimensionality and a specified capacity. The method also includes measuring a second reconstruction loss as a function of the corresponding embedding dimensionality of the variational embedding and estimating a capacity of the reference embedding by comparing the first measured reconstruction loss for the reference embedding relative to the second measured reconstruction loss for the variational embedding having the specified capacity.

Abstract: A system for generating an output audio signal includes a context encoder, a text-prediction network, and a text-to-speech (TTS) model. The context encoder is configured to receive one or more context features associated with current input text and process the one or more context features to generate a context embedding associated with the current input text. The text-prediction network is configured to process the current input text and the context embedding to predict, as output, a style embedding for the current input text. The style embedding specifies a specific prosody and/or style for synthesizing the current input text into expressive speech. The TTS model is configured to process the current input text and the style embedding to generate an output audio signal of expressive speech of the current input text. The output audio signal has the specific prosody and/or style specified by the style embedding.

Abstract: A method for estimating an embedding capacity includes receiving, at a deterministic reference encoder, a reference audio signal, and determining a reference embedding corresponding to the reference audio signal, the reference embedding having a corresponding embedding dimensionality. The method also includes measuring a first reconstruction loss as a function of the corresponding embedding dimensionality of the reference embedding and obtaining a variational embedding from a variational posterior. The variational embedding has a corresponding embedding dimensionality and a specified capacity. The method also includes measuring a second reconstruction loss as a function of the corresponding embedding dimensionality of the variational embedding and estimating a capacity of the reference embedding by comparing the first measured reconstruction loss for the reference embedding relative to the second measured reconstruction loss for the variational embedding having the specified capacity.

Abstract: A method for estimating an embedding capacity includes receiving, at a deterministic reference encoder, a reference audio signal, and determining a reference embedding corresponding to the reference audio signal, the reference embedding having a corresponding embedding dimensionality. The method also includes measuring a first reconstruction loss as a function of the corresponding embedding dimensionality of the reference embedding and obtaining a variational embedding from a variational posterior. The variational embedding has a corresponding embedding dimensionality and a specified capacity. The method also includes measuring a second reconstruction loss as a function of the corresponding embedding dimensionality of the variational embedding and estimating a capacity of the reference embedding by comparing the first measured reconstruction loss for the reference embedding relative to the second measured reconstruction loss for the variational embedding having the specified capacity.

Abstract: A method for estimating an embedding capacity includes receiving, at a deterministic reference encoder, a reference audio signal, and determining a reference embedding corresponding to the reference audio signal, the reference embedding having a corresponding embedding dimensionality. The method also includes measuring a first reconstruction loss as a function of the corresponding embedding dimensionality of the reference embedding and obtaining a variational embedding from a variational posterior. The variational embedding has a corresponding embedding dimensionality and a specified capacity. The method also includes measuring a second reconstruction loss as a function of the corresponding embedding dimensionality of the variational embedding and estimating a capacity of the reference embedding by comparing the first measured reconstruction loss for the reference embedding relative to the second measured reconstruction loss for the variational embedding having the specified capacity.

Abstract: A system for generating an output audio signal includes a context encoder, a text-prediction network, and a text-to-speech (TTS) model. The context encoder is configured to receive one or more context features associated with current input text and process the one or more context features to generate a context embedding associated with the current input text. The text-prediction network is configured to process the current input text and the context embedding to predict, as output, a style embedding for the current input text. The style embedding specifies a specific prosody and/or style for synthesizing the current input text into expressive speech The TTS model is configured to process the current input text and the style embedding to generate an output audio signal of expressive speech of the current input text. The output audio signal has the specific prosody and/or style specified by the style embedding.

Abstract: A method for estimating an embedding capacity includes receiving, at a deterministic reference encoder, a reference audio signal, and determining a reference embedding corresponding to the reference audio signal, the reference embedding having a corresponding embedding dimensionality. The method also includes measuring a first reconstruction loss as a function of the corresponding embedding dimensionality of the reference embedding and obtaining a variational embedding from a variational posterior. The variational embedding has a corresponding embedding dimensionality and a specified capacity. The method also includes measuring a second reconstruction loss as a function of the corresponding embedding dimensionality of the variational embedding and estimating a capacity of the reference embedding by comparing the first measured reconstruction loss for the reference embedding relative to the second measured reconstruction loss for the variational embedding having the specified capacity.

Abstract: A computer-implemented method includes receiving, at a translation server in communication with a network, a request for a translation of text in a source language to a target language. At least a portion of the text is translated at the translation server from the source language to the target language to obtain a translated version of the text in the target language. Translating the text includes determining one or more terms from the text corresponding to a pre-defined term translator, applying the pre-defined term translator to the one or more terms, and translating a set of additional terms from the text from the source language to the target language via a translation model. The method additionally includes providing, via the translation server, the translated version of the text to a web server.

Abstract: A computer-implemented method includes receiving, at a translation server in communication with a network, a request for a translation of text in a source language to a target language. At least a portion of the text is translated at the translation server from the source language to the target language to obtain a translated version of the text in the target language. Translating the text includes determining one or more terms from the text corresponding to a pre-defined term translator, applying the pre-defined term translator to the one or more terms, and translating a set of additional terms from the text from the source language to the target language via a translation model. The method additionally includes providing, via the translation server, the translated version of the text to a web server.

Abstract: A system for searching an object environment includes harvesting and indexing applications to create a search database and one or more indexes into the database. A scoring application determines the relevance of the objects, and a querying application locates objects in the database according to a search term. One or more of the indexes may be implemented by a hash table or other suitable data structure, where algorithms provide for adding objects to the indexes and searching for objects in the indexes. A ranking scheme sorts searchable items according to an estimate of the frequency that the items will be used in the future. Multiple indexes enable a combined prefix title and full-text content search of the database, accessible from a single search interface.

Abstract: A system for searching an object environment includes harvesting and indexing applications to create a search database and one or more indexes into the database. A scoring application determines the relevance of the objects, and a querying application locates objects in the database according to a search term. One or more of the indexes may be implemented by a hash table or other suitable data structure, where algorithms provide for adding objects to the indexes and searching for objects in the indexes. A ranking scheme sorts searchable items according to an estimate of the frequency that the items will be used in the future. Multiple indexes enable a combined prefix title and full-text content search of the database, accessible from a single search interface.

Abstract: A system for searching an object environment includes harvesting and indexing applications to create a search database and one or more indexes into the database. A scoring application determines the relevance of the objects, and a querying application locates objects in the database according to a search term. One or more of the indexes may be implemented by a hash table or other suitable data structure, where algorithms provide for adding objects to the indexes and searching for objects in the indexes. A ranking scheme sorts searchable items according to an estimate of the frequency that the items will be used in the future. Multiple indexes enable a combined prefix title and full-text content search of the database, accessible from a single search interface.

Abstract: A system for searching an object environment includes harvesting and indexing applications to create a search database and one or more indexes into the database. A scoring application determines the relevance of the objects, and a querying application locates objects in the database according to a search term. One or more of the indexes may be implemented by a hash table or other suitable data structure, where algorithms provide for adding objects to the indexes and searching for objects in the indexes. A ranking scheme sorts searchable items according to an estimate of the frequency that the items will be used in the future. Multiple indexes enable a combined prefix title and full-text content search of the database, accessible from a single search interface.