An overview of voice conversion systems

A key notion in numerous data mining and machine learning (ML) algorithms says that the training data and testing data are essentially in the similar feature space and also have the alike probability distribution function (PDF). Though, in several real-life applications, this theory might not retain true. There are issues where training data is costly or tough to gather. Thus, there is a necessity to build high-performance classifiers, trained using more commonly found data from distinct domains. This methodology is stated as transfer learning (TL). TL is usually beneficial when enough data is not available in the target domain but the large dataset is available in source domain. This survey paper explains transfer learning along with its categorization and provides examples and perspective related to transfer learning. Negative transfer learning is also discussed in detail along with its effects on the accomplishment of learning in target domain.

Transfer Learning: Survey and Classification

A system for providing voice-to-text captioning service comprising a relay processor that receives voice messages generated by a hearing user during a call, the processor programmed to present the voice messages to a first call assistant, a call assistant device used by the first call assistant to generate call assistant generated text corresponding to the hearing user's voice messages, the processor further programmed to run automated voice-to-text transcription software to generate automated text corresponding to the hearing user's voice messages, use the call assistant generated text, the automated text and the hearing user's voice messages to train the voice-to-text transcription software to more accurately transcribe the hearing user's voice messages to text, determine when the accuracy exceeds an accuracy requirement threshold, during the call and prior to the automated text exceeding the accuracy requirement threshold, transmitting the call assistant generated text to the assisted user's device for display to the assisted user and subsequent to the automated text exceeding the accuracy requirement threshold, transmitting the automated text to the assisted user's device for display to the assisted user.

Semiautomated relay method and apparatus

Coordinated audio and video filter pairs are applied to enhance artistic and emotional content of audiovisual performances. Such filter pairs, when applied in audio and video processing pipelines of an audiovisual application hosted on a portable computing device (such as a mobile phone or media player, a computing pad or tablet, a game controller or a personal digital assistant or book reader) can allow user selection of effects that enhance both audio and video coordinated therewith. Coordinated audio and video are captured, filtered and rendered at the portable computing device using camera and microphone interfaces, using digital signal processing software executable on a processor and using storage, speaker and display devices of, or interoperable with, the device. By providing audiovisual capture and personalization on an intimate handheld device, social interactions and postings of a type made popular by modern social networking platforms can now be extended to audiovisual content.

Audiovisual capture and sharing framework with coordinated user-selectable audio and video effects filters

Captured vocals may be automatically transformed using advanced digital signal processing techniques that provide captivating applications, and even purpose-built devices, in which mere novice user-musicians may generate, audibly render and share musical performances. In some cases, the automated transformations allow spoken vocals to be segmented, arranged, temporally aligned with a target rhythm, meter or accompanying backing tracks and pitch corrected in accord with a score or note sequence. Speech-to-song music applications are one such example. In some cases, spoken vocals may be transformed in accord with musical genres such as rap using automated segmentation and temporal alignment techniques, often without pitch correction. Such applications, which may employ different signal processing and different automated transformations, may nonetheless be understood as speech-to-rap variations on the theme.

Automatic conversion of speech into song, rap, or other audible expression having target meter or rhythm

The present invention relates to implementing new ways of automatically and robustly evaluating agent performance, customer satisfaction, campaign and competitor analysis in a call-center and it is comprising; analysis consumer server, call pre-processing module, speech-to-text module, emotion recognition module, gender identification module and fraud detection module

Speech analytics system and methodology with accurate statistics

Voice conversion enables generation of a desired speaker's voice from audio recordings of another speaker. In this paper, we focus on a music application and describe the first steps towards generating voices of music celebrities using conventional voice conversion techniques. Specifically, rap singing transformations from English to Spanish are performed using parallel training material in English. Weighted codebook mapping based voice conversion with two different alignment methods and temporal smoothing of the transformation filter are employed. The first aligner uses a HMM trained for each source recording to force-align the corresponding target recording. The second aligner employs speaker-independent HMMs trained from a large number of speakers. Additionally, a smoothing step is devised to reduce discontinuities and to improve performance. The results of subjective evaluations indicate that both aligners perform equivalently well. The proposed smoothing technique improves both similarity to target singer and quality significantly regardless of the alignment method.

Application of voice conversion for cross-language rap singing transformation

In this paper, a machine learning system that automatically detects fraudulent call center conversations is presented. The system first transcribes the call center telephone conversations into text using a speech recognition engine and then it automatically detects the fraudulent conversations by a text-categorization algorithm using the transcribed texts. Several classifiers that use different document vectorizers are trained, tested and their performances are compared. The best results are obtained by using deep convolutional neural networks that use word embedding vectors as their inputs. With these networks, 43% of fraudulent calls can be automatically detected with 62% precision.

Automatic Fraud Detection In Call Center Conversations

This paper presents a Turkish large vocabulary continuous speech recognition (LVCSR) system that automatically transcribes the agent-customer conversations of call centers. The aim is to increase the recognition performance of the system in order to retrieve correct statistics from the call center conversations. For this reason, a confidence metric which is calculated from the outputs of two decoders that uses different language model units is also proposed. Tested on real call center data, the system results in 22.5% word error rate on agent speech, and 40.7% word error rate on customer speech. When the proposed confidence metric is used, customer and agent word recognition rates absolutely increases up to 19.4%, and 12.3% respectively, while nearly 70% of the call data is still covered.

Turkish LVCSR system for call center conversations

This paper presents a method of language model adaptation for call-center conversations using automatic speech recognition (ASR) transcripts and their confidence scores. The goal is to select the optimal adaptation set by estimating the recognition errors and minimizing the adaptation language model (LM) perplexity. ASR transcripts are ranked with respect to their confidence scores and adaptation data selection is done iteratively by filtering the most reliable transcript set that minimizes the LM perplexity. Model adaptation is then carried out by interpolating the selected adaptation LM with the baseline in-domain LM. We have evaluated our approach on agent speech of real call-center conversations and experiments show that 4% relative word error rate reduction is achieved with the proposed approach.

Language model adaptation for automatic call transcription

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