Google Cloud Speech-to-Text

Earlier we used to completely rely on the notepad or scribble-based notebook during the call to capture the important discussion, but that seems to be hectic and time-confusing. While documenting itself is a very big task, we got a solution to this via Google Cloud Speech-to-Text. Where it has very great features like capturing the audio and converting the data to text. Also, it helps in making our documentation and knowledge management easier. That way we can share the same information across different teams without the manual effort. Below are the couple of business problems that were been addressed via Google Cloud Speech-to-Text, like manual transcription overhead and improving customer experience.

• Ability to expand the transcription to new languages and regions expand multilingual customer support enables consistent processes across international teams
• More reliable downstream analytics and clearer data for compliance audits
• Reduces turnaround time from days/hours to minutes and cuts cost per transcribed minute dramatically

Webex Meetings, Microsoft Teams Rooms, Google Meet

As a senior project manager and being a part of core project management team, I use Google Cloud as my daily driver to improve the speed and accuracy of communication workflows across our projects. One of the biggest challenges I face is quickly converting meeting discussions, voice notes, and brainstorming ideas into a well-written format. This problem gets bigger when meeting multiple clients meetings and field visits. I often lead calls and record voice memos for further plan of action and get prominent points from our esteemed clients which become absolute in achieving milestones. These are also shared to them as well as Minutes of Meeting. All these are being done with the help of a Speech to Text transcriber, which leads to preparing Actionable reports, Project Briefs, and follow-up summaries. It is also very helpful in multitasking, whether I am taking status updates, project documentation, and communication with remote team members. I can easily dictate them updates, feedback and the tool easily transcribes them accurately within few seconds. Our team also use it to generate subtitles and or text documentation for training videos and internal walkthroughs. All this has saved us several hours manual work and has significantly reduced our dependency on hand written notes or does not require to recall post meeting discussions. It also ensures that we will not miss any key points from our planning and strategy sessions. I had integrated into our daily project operations and have streamlined both internal and external communications. It becomes an essential part of workflow and specially for individual like me who prefers speaking over typing.

• I had saved almost 60 to 70 percent of time that i spent on documentation. Tasks like writing meeting summaries, drafting reports are now completed in just 30 to 40 minutes of time which generally takes more then 2 hours.
• It handles my natural speech correctly which is way better then manual typing or utilizing any third party tools.
• Our internal workflows are way smoother now as i use this tool to transcribe notes across multiple departments without any delays. It eventually helps in maintaining the momentum in decision making and follow ups.

Otter.ai

Canva, Grammarly, Notion

Previously, converting the speech to text seemed very time-consuming. The team often needed quick access to the information from the calls, and this real-time transcription enables faster decision-making and keeps the process smoother. Certain times it's very hard and difficult to analyze the large volume of the data. Once the audio is converted into text, we can easily search for any keyword and perform data analysis, as a result of which it will help in improving the report. We as a technical support team use this tool daily to convert the customer conversations into text for quality checking purpose and sentimental analysis We also use this tool for transforming the audio of our field offers into text.

• Great accuracy and consistency, where Google Speech to Text provides more consistent results and fewer errors with common accents.
• Significant reduction in the manual transcription as a result of which lower labour cost and faster processing time which improves efficiency and reduces delay in the downstream applications
• There is a scope of accuracy drop in very noisy and overlapping speech environments; as a result, transcription accuracy decreases, requiring manual correction
• Unless usage is optimised or reduced, ongoing costs for higher volume data and usage will be considered high

Webex Connect

Microsoft Teams, Google Hangouts (Classic), Slack

It is an API that helps to convert speech to text. I have integrated this API into an Android wearable device used in a warehouse and it performs well.

• Noise cancellation should be improved
• Accents should be captured based on the region and location, and the API should work accordingly.
• Doesn't works well with the multiple speakers
• If we pronounce correctly and accurately, everything works fine.

Amazon Transcribe and IBM Watson Speech to Text

IBM Watson Speech to Text, Amazon Transcribe

I prefer Google Cloud Speech to Text for translating people's queries because my team members are from different countries, and I need to communicate with them effectively. So, it's good to understand their language and speak with them. Apart from that, I implemented its API in my various Python scripts to automate my virtual assistant in different languages. Its custom models and phrase hints improve the accuracy and maintain the process well. Sometimes I also used it for my YouTube video subtitles and podcasts. We can use it in many ways and enhance our capability to work in extreme conditions.

• It saved us a lot of time and money by eliminating the need to transcribe meetings, interviews, and general discussions.
• If I talk about my office team, it gives me the power to understand the language of each member, and now I'm not forcing them to translate it into my language.
• The best part is that it freed us to focus on other aspects, such as innovation, and elaborate on our thoughts, because now we don't have to worry about language; we just need to express our ideas.

Microsoft Azure, OpenAI API and Amazon CodeWhisperer

Microsoft Azure, Amazon Athena, OpenAI API