Abstract: A method, performed by a device, of providing a response message to a user input includes obtaining location information of the device; executing a service providing agent corresponding to the location information; receiving a speech input from a user; generating the response message based on the received speech input, the response message being related to a service provided by the executed service providing agent; and displaying the generated response message, wherein the executed service providing agent generates the response message using a model trained using an artificial intelligence (AI) algorithm, the trained model being one from among a plurality of trained models each corresponding to a respective service from among a plurality of services provided by a respective service providing agent from among a plurality of service providing agents, and wherein the trained model corresponds to the executed service providing agent.

Abstract: A method, system and computer program product for improving the understanding of chat transcript data. Chat transcripts are analyzed to classify the utterances into intents and identify products discussed in the chat transcripts. The data of the chat transcripts are divided into categories of utterances associated with products and intents by applying tags to the chat transcripts. The categories of utterances associated with products and intents are then clustered into clusters based on sentence similarity. Once the utterances are grouped, a representative utterance is extracted from a cluster, where the representative utterance is an utterance that has the highest semantic similarity to the utterances in the cluster. In this manner, users will be provided a more accurate guide as to the underlying meaning of the chat transcript data thereby improving the understanding of the chat transcript data more efficiently and accurately than current chat transcript analysis tools.

Abstract: A speech recognition method is provided. The method includes: obtaining a voice signal; processing the voice signal according to a speech recognition algorithm to obtain n candidate recognition results, the candidate recognition results including text information corresponding to the voice signal; identifying a target result from among the n candidate recognition results according to a selection rule selected from among m selection rules, the selection rule having an execution sequence of j, the target result being a candidate recognition result that has a highest matching degree with the voice signal in the n candidate recognition results, an initial value of j being 1; and identifying the target result from among the n candidate recognition results according to a selection rule having an execution sequence of j+1 based on the target result not being identified according to the selection rule having the execution sequence of j.

Abstract: Encoding/decoding an audio signal having one or more audio components, wherein each audio component is associated with a spatial location. A first audio signal presentation (z) of the audio components, a first set of transform parameters (w(f)), and signal level data (?2) are encoded and transmitted to the decoder. The decoder uses the first set of transform parameters (w(f)) to form a reconstructed simulation input signal intended for an acoustic environment simulation, and applies a signal level modification (?) to the reconstructed simulation input signal. The signal level modification is based on the signal level data (?2) and data (p2) related to the acoustic environment simulation. The attenuated reconstructed simulation input signal is then processed in an acoustic environment simulator. With this process, the decoder does not need to determine the signal level of the simulation input signal, thereby reducing processing load.

Abstract: A device, system, and method whereby a speech-driven system can distinguish speech obtained from users of the system from other speech spoken by background persons, as well as from background speech from public address systems. In one aspect, the present system and method prepares, in advance of field-use, a voice-data file which is created in a training environment. The training environment exhibits both desired user speech and unwanted background speech, including unwanted speech from persons other than a user and also speech from a PA system. The speech recognition system is trained or otherwise programmed to identify wanted user speech which may be spoken concurrently with the background sounds. In an embodiment, during the pre-field-use phase the training or programming may be accomplished by having persons who are training listeners audit the pre-recorded sounds to identify the desired user speech. A processor-based learning system is trained to duplicate the assessments made by the human listeners.

Abstract: A computer implemented method, apparatus, and computer program product for a sound system. Speech recognition is performed on input audio data comprising speech input to a sound system. Speech recognition is additionally performed on at least one instance of output audio data comprising speech reproduced by one or more audio speakers of the sound system. A difference between a result of speech recognition performed on the input audio data and a result of speech recognition performed on an instance of corresponding output audio data is determined. The quality of the reproduced speech is determined as unsatisfactory when the difference is greater than or equal to a threshold. A corrective action may be performed, to improve the quality of the speech reproduced by the sound system, if it is determined that the speech quality of the reproduced sound is unsatisfactory.

Abstract: The invention discloses a multi-language mixed speech recognition method, which belongs to the technical field of speech recognition; the method comprises: step S1, configuring a multi-language mixed dictionary including a plurality of different languages; step S2, performing training according to the multi-language mixed dictionary and multi-language speech data including a plurality of different languages to form an acoustic recognition model; step S3, performing training according to multi-language text corpus including a plurality of different languages to form a language recognition model; step S4, forming the speech recognition system by using the multi-language mixed dictionary, the acoustic recognition model and the language recognition model; and subsequently, recognizing mixed speech by using the speech recognition system, and outputting a corresponding recognition result.

Abstract: Provided is a system including an electronic device to recognize and process a user's speech and a method of controlling speech recognition on an electronic device. According to an embodiment, an electronic device comprises a communication circuit, an input module, at least one processor, and a memory operatively connected with the at least one processor the input module, and the communication circuit, wherein the memory stores instructions configured to enable the at least one processor to provide a function according to a first utterance of a user for wake-up, receive a second utterance of the user including a plurality of words with predesignated relevance through the input module while the function is provided, transmit information about the second utterance of the user, to another electronic device via the communication circuit, and receive a response related to the second utterance of the user from the second electronic device according to the transmission and provide the received response.

Abstract: A dialogue intent analyzer uses a conversation between a user and agent, and intents in the conversation to predict a set of answers that better respond to pending user question. The dialogue intent analyzer understands the context surrounding the pending question by capturing and modeling prior conversation and intents within the conversation. Dialogue intent analyzer also reduces genericness in predicted answers by weighting previously used answers based on their frequency and length. Dialogue intent analyzer also increases diversity of predicted answers by using a diverse beam search.

Abstract: A method may include obtaining audio of a communication session between a first device of a first user and a second device of a second user. The method may further include obtaining a transcription of second speech of the second user. The method may also include identifying one or more first sound characteristics of first speech of the first user. The method may also include identifying one or more first words indicating a lack of understanding in the first speech. The method may further include determining an experienced emotion of the first user based on the one or more first sound characteristics. The method may also include determining an accuracy of the transcription of the second speech based on the experienced emotion and the one or more first words.

Abstract: An approach is provided to use a first translation attribute that is received at a user interface from a user to automatically translate a document. The source document that is in a source natural language is translated to a target document that is in a target natural language by using a machine translator that utilizes the first translation attribute, such as adequacy or fluency. The target document is analyzed with the analysis resulting in a second translation attribute (e.g., either adequacy or fluency, whichever is different from the first translation attribute). The target (translated) document and the second translation attribute are then provided to the user, such as at the user interface.

Abstract: Systems and methods for identifying content corresponding to a language are provided. Language spoken by a first user based on verbal input received from the first user is automatically determined with voice recognition circuitry. A database of content sources is cross-referenced to identify a content source associated with a language field value that corresponds to the determined language spoken by the first user. The language field in the database identifies the language that the associated content source transmits content to a plurality of users. A representation of the identified content source is generated for display to the first user.

Abstract: An information processing device receives a specification of a word, and specifies an appearance position associated with the specified word by referring to information in which appearance positions in text data of respective words included in the text data are associated with each of the words. The information processing device specifies an attribute associated with the specified appearance position by referring to information in which attributes at respective appearance positions in the text data of respective words included in the text data are associated with the appearance positions.

Abstract: Data bus includes a device controller coupled to a first interface for digital communications using a first communications protocol, the device controller including a master bus controller controlling a multi-drop bus using a second communications protocol, and a slave device coupled to the multi-drop bus and configured to transmit and receive digital communications with the device controller using the second communications protocol. Each transmission line end is terminated using a device attached at one end of the transmission line and by another device attached at the other end. The second communications protocol supports multiple data rates using a fixed frame format. Safe synchronization may be established by systematically eliminating all false positions instead of searching for a valid candidate. Noise in the audio band may be lowered by scrambling of the data using a pseudo-random generator.

Abstract: Exemplary embodiments relate to improvements in spoken language understanding (SLU) systems. Conventionally, SLU systems include an automatic speech recognition (ASR) component configured to receive an input of audio data and to generate a textual representation of the audio data. Conventional SLU systems also include a natural language understanding (NLU) component configured to receive a text-based transcript and perform language-based tasks such as domain classification, intent determination, and slot-filling. However, these two components are typically trained separately based on different metrics. In real-world situations, errors in the ASR component propagate to the NLU component, which degrades the performance of the overall system. Exemplary embodiments described herein perform SLU in an end-to-end manner that infers semantic meaning directly from audio features without an intermediate text representation.

Abstract: A feeling estimation device includes: a first acquirer configured to acquire sound data of an utterance of a user; a second acquirer configured to acquire text data obtained by converting the sound data acquired by the first acquirer into text; a first estimator configured to merge an index value based on the sound data acquired by the first acquirer and an index value based on the text data acquired by the second acquirer via first fusion and estimate a feeling of the user on the basis of the merged index value; and a second estimator configured to merge an index value indicating a result of the estimation performed by the first estimator and the index value based on the text data acquired by the second acquirer via second fusion and estimate a feeling of the user on the basis of the merged index value.

Abstract: Method and apparatus for training and using a classifier for words. Embodiments include receiving a first plurality of sentences comprising a first word that is associated with a class and a second plurality of sentences comprising a second word that is not associated with the class. Embodiments include training a classifier using positive training data for the class that is based on the first plurality of sentences and negative training data for the class that is based on the second plurality of sentences. Embodiments include determining a measure of correlation between a third word and the class by using a sentence comprising the third word as an input to the classifier. Embodiments include using the measure of correlation to perform an action selected from the following list: selecting content to provide to a user; determining an automatic chat response; or filtering a set of content.

Abstract: A method, a system, and a computer program product are provided. Speech signals from a medical conversation between a medical provider and a patient are converted to text based on a first domain model associated with a medical scenario. The first domain model is selected from multiple domain models associated with a workflow of the medical provider. One or more triggers are detected, each of which indicates a respective change in the medical scenario. A corresponding second domain model is applied to the medical conversation to more accurately convert the speech signals to text in response to each of the detected one or more triggers. The corresponding second domain model is associated with a respective change in the medical scenario of the workflow of the medical provider. A clinical note is provided based on the text produced by converting the speech signals.

Abstract: A biometric is formed for at least one enrolled speaker by: obtaining a sample of speech of the enrolled speaker; obtaining a measure of a fundamental frequency of the speech of the enrolled speaker in each of a plurality of speech frames; and forming a first distribution function of the fundamental frequency of the speech of the enrolled speaker. Subsequently, for a speaker to be recognised, a sample of speech of the speaker to be recognised is obtained. Then, a measure of a fundamental frequency of the speech of the speaker to be recognised is obtained in each of a plurality of speech frames. A second distribution function of the fundamental frequency of the speech of the speaker to be recognised is formed, the second distribution function and the first distribution function are compared, and it is determined whether the speaker to be recognised is the enrolled speaker based on a result of comparing the second distribution function and the first distribution function.

Abstract: Methods and systems for automating user interactions include annotating chat logs to identify user intent in question-answer pairs. A classifier is trained, using the annotated chat log, to identify user intent in automated conversations. Chat flows are formed, using the annotated chat logs, that provide responses to user statements based on identified user intent. An automated conversation is conducted with a user, using the chat flows and the classifier, to provide automated responses to user statements.