Abstract: A computer-implemented method dynamically routes and processes user queries in a bot-builder system. The method involves receiving a user query and routing the user query to a Personal Info Retrieval Module, a Document Retrieval Module, and a Tool Manager. The Personal Info Retrieval Module retrieves and updates personal information in real time through API calls. The Document Retrieval Module and Tool Manager retrieve document and tool embeddings from corresponding vector stores. An AI model processes the user query to generate query embeddings, performing similarity searches against the document and tool embeddings. The retrieved information is combined with the user query and chat history to form a comprehensive input, which the AI model processes to determine an appropriate response. The response is generated using existing knowledge or by invoking an external tool. The system includes real-time updates, leveraging historical interaction records and external services and integration of external tools.

Abstract: Methods and systems for generating and employing synthetic data are disclosed. The synthetic data is generated by defining roles for a plurality of speakers and inputting the roles to at least one Large Language Model (LLM), which in turn successively generates statements of each speaker which are responsive to generated statements for the other speaker based on the defined roles. Each successive set of statements are input to the LLM to generate additional statements of the speakers to obtain synthetic dialog data. The synthetic dialog data can be used to test and/or train neural networks as well as various platforms, including conversation analytics platforms.

Abstract: An automatic online voice translation system and method, comprising a virtual translator, a SIP gateway, and a storage, wherein the SIP gateway receives a call from a caller, when the virtual translator is not activated, the SIP gateway establishes a communication between the caller and the agent by streaming in real-time caller's voice directly to the agent and agent's voice directly to the caller, and when the virtual translator is activated, the SIP gateway creates audio files based on the received voice streams of both the caller and the agent, and sends the created audio files to the virtual translator for translating the caller's voice into a language understood by the agent, before transmitting the voice to the agent. In another embodiment, a contact center platform is used for applying features such as call recording, Interactive Voice Response (IVR) service, and conferencing service to the call.

Abstract: An automatic donor selection algorithm estimates the subjective voice conversion output quality from a set of objective distance measures between the source and target speaker's acoustical features. The algorithm learns the relationship of the subjective scores and the objective distance measures through nonlinear regression with an MLP. Once the MLP is trained, the algorithm can be used in the selection or ranking of a set of source speakers in terms of the expected output quality for transformations to a specific target voice.

Abstract: The conversion of speech can be used to transform an utterance by a source speaker to match the speech characteristic of a target speaker. During a training phase, utterances corresponding to the same sentences by both the target speaker can source speaker can be force aligned according to the phonemes within the sentences. A target codebook and source codebook as well as a transformation between the two can be trained. After the completion of a training phase, a source utterance can be divided into entries in the source codebook and transformed into entries in the target codebook. During the transformation, the situation arises where a single source codebook entry can have several target codebook entries. The number of entries can be reduced with the application of confidence measures.