Voice

In NLP and linguistics, anaphora refers to the use of a word or phrase that refers back to a previous word or phrase, often using pronouns like “he,” “she,” or “it.” The goal of anaphora resolution is to determine what the anaphoric expression is referring to, which can be crucial for understanding the meaning of a sentence or text. ​​Example: “John gave his book to Mary. She was very happy with it.” In this example, the anaphora “She” refers back to “Mary,” and the anaphora “it” refers back to “his book.”

ASR is the technology underlying speech-to-text conversion. It uses machine learning algorithms and statistical models to recognize patterns in speech and convert them into written language. ASR systems can be trained on large datasets of speech recordings and their corresponding transcripts to improve their accuracy.

In conversational systems, barge-in refers to the ability of a user to interrupt a system’s response or prompt with their own input, usually by speaking over the system. This feature allows users to quickly correct errors, provide additional information, or change the direction of the conversation.

In audio signal processing, beam forming is a technique used to enhance the quality of audio signals captured by multiple microphones. By combining the signals from multiple microphones, beam forming creates a virtual microphone that focuses on a specific area or direction, reducing background noise and improving the signal-to-noise ratio.

Feedback provided to the user after a voice command has been executed, confirming the action taken.

The ability of a voice input system to understand the context in which a command is given, including previous interactions and environmental factors.

The process of clarifying ambiguous or unclear voice commands, often through follow-up questions or prompts.

Strategies for dealing with errors or misinterpretations in voice input, such as providing feedback or offering alternatives.

Large Language Models (LLMs) are a type of artificial intelligence (AI) model that is trained on vast amounts of text data to learn patterns, relationships, and structures within language. These models are designed to process and generate human-like language, enabling them to perform a wide range of natural language processing (NLP) tasks.

The delay between the user speaking and the system responding to their voice input.

Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that deals with the interaction between computers and humans in natural language. It involves the development of algorithms, statistical models, and machine learning techniques to process, understand, and generate natural language data.

Speech recognition, also known as voice recognition, refers broadly to the ability of a system to identify and process spoken language. It encompasses ASR (transcription) as well as related capabilities like speaker identification and intent recognition.

The process of converting spoken language into written text, also known as speech recognition or automatic speech recognition (ASR).

The ability of a computer system to synthesize spoken words from text, using techniques such as concatenative synthesis, parametric synthesis, and generative AI.

A type of interaction where the user speaks and then waits for the system to respond before speaking again.

A Voice User Interface (VUI) is a type of user interface that allows users to interact with a system, device, or application using voice commands. VUIs use speech recognition technology to interpret and process spoken language, enabling users to perform tasks, access information, and control devices without the need for physical input such as typing or clicking.

A specific word or phrase used to activate a voice input system, such as “Hey Meta”.

Provide clear mic activation awareness through earcons or conversational cues.

Offer users the option to opt-in to voice experiences where their voice will be captured, respecting their privacy.

Limit the amount of information users need to carry in their heads. Use breadcrumbing techniques to guide users through complex interactions. Breadcrumbing refers to the process of providing users with a clear and consistent navigation path through a voice-based conversation. This provides acknowledgement of their current position and prevents confusion and anxiety.

Provide users with a clear understanding of how their audio data is being used and protected, including the ability to revoke consent or delete their data.

Design voice interactions that adapt to the user’s context, considering both visual and audio-only experiences. Use passthrough or immersive experiences and embodied interactions to create a seamless experience.

Implement a system for handling and repairing errors that acknowledges the disconnect and provides options for solving the error. Structure prompts to guide users towards resolving errors and provide clear instructions for repair. Prioritize user understanding and resolution in the design of the error handling system.

Educate users about new interaction modes and mental models, especially for NPC (Non-Player Character) interactions, through a robust NUX (New User Experience). Teach users how to prompt effectively and empower creators to take responsibility for prompting in Horizon. Frame education in the context of evolving mental models to help users adapt to new technologies.