Abstract: A system for making polymeric fibers can comprise a main extruder configured to receive a polymer feedstock composition. The main extruder can have an outlet in fluid communication with a transfer line configured to receive a polymer melt from the main extruder and transfer the polymer melt in a flow direction that is towards a spinneret. A side arm extruder assembly can be positioned along the transfer line between the main extruder and the spinneret. The side arm extruder assembly can comprise an outlet positioned within the transfer line to inject a colored polymer melt concentrate into the transfer line.

Abstract: Natural language grammars interpret expressions at the conversational human-machine interfaces of devices. Under conditions favoring engagement, as specified in a unit of conversational code, the device initiates a discussion using one or more of TTS, images, video, audio, and animation depending on the device capabilities of screen and audio output. Conversational code units specify conditions based on conversation state, mood, and privacy. Grammars provide intents that cause calls to system functions. Units can provide scripts for guiding the conversation. The device, or supporting server system, can provide feedback to creators of the conversational code units for analysis and machine learning.

Abstract: Real-time speech recognition systems extend an end-of-utterance timeout period in response to the presence of a disfluency at the end of speech, and by so doing avoid cutting off speakers mid-sentence. Approaches to detecting disfluencies include the application of disfluency n-gram language models, acoustic models, prosody models, and phrase spotting. Explicit pause phrases can also be detected to extend sentence parsing until relevant semantic information is gathered from the speaker or another voice. Disfluency models can be trained such as by searching by successive deletion of tokens, phonemes, or acoustic segments to convert sentences that cannot be parsed into ones that can. Disfluency-based timeout adaptation is applicable to safety-critical systems.

Abstract: Natural language grammars interpret expressions at the conversational human-machine interfaces of devices. Under conditions favoring engagement, as specified in a unit of conversational code, the device initiates a discussion using one or more of TTS, images, video, audio, and animation depending on the device capabilities of screen and audio output. Conversational code units specify conditions based on conversation state, mood, and privacy. Grammars provide intents that cause calls to system functions. Units can provide scripts for guiding the conversation. The device, or supporting server system, can provide feedback to creators of the conversational code units for analysis and machine learning.