The CEO of rising conversational AI platform provider, Hyro, joins us to share insights on the unique approach taken to create its NLU technology and the kind of results its seeing in market.

Israel Krush shares the ins and outs of Hyro's philosophy, its intentness NLU approach and the importance of knowledge management.

We get into why, when using Hyro, there's less of a need for designing conversational flows and how the technology is able to make decisions about what questions to ask next based on the data. This is because of a combination of a linguistic approach to NLU creation and through pairing that with a bespoke knowledge graph with documents content, entities and the relationship between them.

We discuss some of the challenges organisations have when implementing conversational AI, how Hyro is helping clients overcome them and how a seemingly straightforward deployment is managed and maintained.

Deepgram is a Speech Company whose goal is to have every voice heard and understood. We have revolutionized speech-to-text (STT) with an End-to-End Deep Learning platform. This AI architectural advantage means you don’t have to compromise on speed, accuracy, scalability, or cost to build the next big idea in voice. Our easy-to-use SDKs and APIs allow developers to quickly test and embed our STT solution into their voice products. For more information, visit https://deepgram.com/vuxworld.

Links:

https://hyro.ai

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Deepgram is a Speech Company whose goal is to have every voice heard and understood. We have revolutionized speech-to-text (STT) with an End-to-End Deep Learning platform. This AI architectural advantage means you don’t have to compromise on speed, accuracy, scalability, or cost to build the next big idea in voice. Its easy-to-use SDKs and APIs allow developers to quickly test and embed our STT solution into their voice products.

We sat down with CEO, Scott Stephenson and discussed all things speech recognition including;

• how a particle physicist turned into a speech recognition expert,
• how Deepgram approaches creating ASR,
• what use cases their customers are utilising the service for,
• the different between Deepgram and it's big tech competitors,
• why deep learning is an important differentiator,
• why speed is important and how quick its response times are,
• accuracy levels and training/tuning ASR,
• how to select a speech recognition vendor and much much more

Visit Deepgram.com

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Kordel France discusses how Seekar Technologies is approaching AI and how it's using its unique technologies and multi-model processing of a variety of input signals (from speech, to vision, to radar and other signals) and helping disadvantaged industries such as medicine, agriculture and manufacturing solve some core problems.

From using speech recognition to detect whether someone is lying, to mental health screening and how combining voice AI with other inputs and modalities can have a huge impact on the value of the solution.

Seekar's approach is to reduce bias in AI processing through it's 'explainable' AI developer features that allow teams to understand the specific parts of a neural network and highlight which areas are performing well and poorly given a variety of input sources (such as different gendered voices).

Check out Seekar Technologies

Connect with Kordel France on LinkedIn

Kordel France on Twitter

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TalkVia, the voice assistant design and development platform for marketing teams looking to invest in emerging technologies like Alexa and Google Assistant, conducted the Smart Audio Report, Australia, to understand the adoption and usage levels of voice assistant technologies down under.

Head of Innovation, Guy Munro, joins us to share the details and insights from the survey, how voice assistant adoption in APAC is similar or different to the US and EU, as well as his perspectives and advice for marketing teams looking to explore these emerging channels.

Links

Check our TalkVia

Download the Smart Audio Report, Australia

Connect with Guy on LinkedIn

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Voice AI can provide immense value outside of the home. Problem is, outside is noisy! So how to you create voice AI solutions and speech recognition systems that work well in cars, in traffic, outside or with screaming kids in the background!?

We're joined by SoundHound's Director of Business Development, Andrew Richards, to shed some light on how one of the top voice AI companies tackles this challenge.

We dive into:

• What are the common challenges when implementing voice solutions in noisy environments?
• Any examples of areas where the environment proves trickier than others? 
• Microphone technology, placement and arrays
• What's beam forming and why is it important?
• How do you (or what do you do to...) mitigate the noise to get a clean signal? 
• About additive noise, convolutional noise, distortion and noise reduction
• What do you need to consider when implementing a voice assistant in a noisy environment? 
• How to train speech recognition systems for use in noisy places?

Visit Houndify.com

Read the Houndify Speech to Meaning blog (recommended)

All about speech recognition with Catherine Breslin

How voice AI is disrupting industries with SoundHound's Mike Zagorsek

How to train speech recognition systems with Atexto

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Every experienced conversation designer has been asked this question: how do I get into conversation design?

Heyday's Team Lead for Conversation Design, Anna Ralph, joins us to answer that question and more as we chart her journey from copywriter to conversation designer and share how you can do the same.

Links

Heyday by Hootsuite

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Anyone who has worked with semi-complex conversational AI from a design or development perspective has hit the limitations of the technology. 'If this then that' statements that determine logic, handling 'no match' intents and providing training data and synonyms for intents and slots. All of this gets incredibly complex and difficult to manage when you get into anything beyond a simple use case.

One company that's pushing the boundaries of conversational AI is Wluper. Today we're joined by co-founder and CTO, Nikolai Rozanov to discuss Wluper's approach to an intentless NLU, advanced memory and dialogue management. This is the next generation of conversational AI.

Links

Connect with Nikolai Rozanov

Check out Wluper

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Genesys is one of the largest cloud CX providers on the planet and is leading the way in the future of customer experience with is cloud contact centre and automation platforms.

VP, Product Marketing for AI at Genesys, Elcenora Martinez joins us to share her experience and insights on the current best practice for implementing conversational AI in enterprise. We discuss the approach Genesys has taken to its voice and conversational AI product offering, the Bold 360 acquisition, trends and observations on challenges faced by companies looking to implement conversational AI and how to overcome them, as well as the importance of design thinking on AI projects and programmes.

Download the Connected Custom Experience study

Connect with Elcenora Martinez on LinkedIn

Try Genesys Dialogue Engine

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It's hard enough designing conversational AI agents that understand single line utterances and are able to string together a conversation with a person. Try monitoring both a caller and an agent having a two way conversation and understanding it to the extend that you're able to offer real time, in the moment, accurate guidance to the call handler.

This is exactly what Balto does. We chat to CEO, Marc Bernstein, about where the idea for Balto came from, how he went about creating the company, the business and technical challenges he and the team had to overcome and we get into details on how the Balto solution is helping contact centre agents be more productive.

Balto.ai

Connect with Marc Bernstein on LinkedIn

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Voice assistants and voice user interfaces are becoming ubiquitous across all device types. Voice is quickly becoming the modality of choice for finding information and getting things done. But with every new device type, comes a different type of customer experience, based on different mental models, different expectations, different context and different goals.

One of the most promising device types for voice user interfaces is the TV. With a wealth of content options from a range of providers, there’s always something on. The problem is finding it.

We’ve all tried searching manually with a remote control. That’s not something one enters into lightly. It’s clunky, takes forever and the search capabilities are extremely basic, typically. Then, how do users even know that they can use their voice in the first place? And how do they discover what it can do and the features it has?

These are just some of problems that the Voice AI team at Comcast is solving, and the way that they’re solving it has lessons for all voice and conversational AI practitioners and leaders looking to set up a conversational AI Centre of Excellence.

Learn how to approach designing a voice experience for different modalities, what you should consider, how to define use cases, how to approach user research and problem identification, how to prototype and test methodologies, as well as how to measure success.

Join Chuong Nguyen, Product Designer, and Carolyn Reed, Lead Voice Researcher, Comcast, on VUX World to find out.

Chuong Nguyen on LinkedIn

Carolyn Reed on LinkedIn

Cheryl Platz; Design beyond devices

Jake Knapp; Sprint

Voicebot.ai

Women In Voice

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