CredAbility, a national leader in non-profit credit counseling, uses Verint recording and quality monitoring and Impact 360 Workforce Management to generate metrics for continuous improvement, optimize the productivity of staff, and deliver a more positive customer experience. CredAbility, a national leader in non-profit credit counseling, uses Verint recording and quality monitoring and Impact 360 Workforce Management to generate metrics for continuous improvement, optimize the productivity of staff, and deliver a more positive customer experience.
Salsa and afro cuban montunos for piano pdf. 475.2 mb when unzipped.- MP3 Salsa Percussion Library: 22 HQ Percussion only Audio Tracks in 2-3 and 3-2 clave. Audio track tempos from 132bpm to 212bpm. Great for practicing, songwriting and music production. 382.4 mb when unzipped.You will receive a download link to the eBook (PDF), MP3 and MIDI Libraries. Salsa, Afro Cuban Montunos for Piano ( e-Book, MIDI and Extended MP3 Library)Digital Download: PDF, MIDI and MP3 Files.- Salsa, Afro Cuban Montunos for Piano (e-Book, PDF file). 93 pages, 44.3mb- MIDI Library: MIDI Files of every example in the book. 304 MIDI Files!- MP3 Library: MP3 Files of every example in the book. 304 MP3 Files!
What is Speech Analytics?Speech analytics is at the forefront of the corporate push to make intelligence gained from Big Data not only valuable but actionable in real-time. Speech analytics offers the ability to create meaningful voice data and interaction trends to help companies improve services, reduce costs, and grow revenue in their contact center and other business areas.Originally called audio-mining, in which audio files were converted to text to enable searches of specific words or phrases, speech analytics now involves in-depth searches based on phonetics with the ability to detect certain emotions expressed on a phone call as well as trends within a call, such as hold times, silent patches, or agents talking over a caller. Old audio-mining techniques offered matching accuracy rates of around 50 percent. Current speech analytics technology boasts accuracy significantly greater than 80 to 90 percent.
With improved accuracy, speech analytics have been working diligently to improve the speed at which results are delivered. Intelligence can be provided in near real time to the business decision makers. As a result of improved technology and capabilities, speech analytics is beginning to mature but is still in the early adoption phases within the call center market. The Market for Speech AnalyticsThe speech analytics market has been growing at a strong clip since it came on the scene in 2004. Barriers to growth have included low customer awareness and a lack of understanding about quantifiable return on investment (ROI). Speech analytics vendors now are targeting different segments, including small- to mid-market centers and are providing more customized solutions to boost adoption rates. In addition, hosted or SaaS solutions can significantly reduce the initial investment and financial commitment to deploying a speech analytics product. Significant growth is expected throughout the near future.
In fact, speech analytics implementations in contact centers have increased from 200 in 2005 to 1,200 in 2007 to 3,600 in 2012 while the number of seats has grown from 176,825 in 2006 to 2.3 million through 2013. Growth of 18 percent is expected from 2014 through 2016. The advent of real-time speech analytics solutions is helping to drive this growth, especially in the healthcare and account collections segments.Despite these market developments, the flagship segment for these systems continues to be in the security, law enforcement, and intelligence gathering communities. In the traditional call center market, only 3.6 percent of all call center seats have the benefit of speech analytics packages. But the ability of speech analytics to convert unstructured data - which represents about 90 percent of all enterprise data - to structured metadata and the capability to work throughout the organization make speech analytics a viable investment.With the ability to increase revenue and customer loyalty and to provide direct and relevant feedback to other areas of the enterprise - coupled with the current boom in the Big Data segment - investments in speech analytics are expected to expand by nearly 30 percent each of the next three years.
Now that service providers are offering real-time speech analytics solutions, the interest that companies have is increasing because they can impact the outcome of a customer interaction that is occurring in that moment. As with any technology implementation, however, caveats exist. Processes, good management practices, and other technologies must already be in place to ensure successful deployment.Some firms that have implemented speech analytics specifically in the contact center are touting return on investment within seven to nine months; however, those organizations that take their time to plan for their speech analytics solution to impact the whole enterprise are boasting investment returns within four months. If that is indeed true, it bodes well for the industry. Early adopters, however, tend to put a lot of resources into ensuring that their investment is beneficial by applying it to gain an advantage over their competition. Later adopters are often more lax in how they train managers and supervisors to apply the technology, and may even neglect to commit dedicated resources to leverage the technology to improve processes and quality delivery.
History of Speech AnalyticsOriginally, speech analytics was used by government organizations to track the use of key words or phrases to help identify security risks or threats by individuals or entities under surveillance. The earliest versions of this technology were quite simple and known as audio-mining or word spotting. Audio-mining applications indexed the speech from an audio or video file by processing it through a large vocabulary recognizer and by converting it into searchable text files. The words or key phrases were predefined, and an operator was notified only if a match existed. Accuracy rates were generally less than 50 percent. Yu gi oh episode 2 sub indo cars 2.
Accuracy rates using speech-to-text systems have increased dramatically in recent years, however.As technology improved, organizations demanded search capabilities based on phonetics to improve audio-mining accuracy. With this phonetics-based method, an index of phonetic content, as opposed to the word content requiring letter-for-letter matches, is created. As a result, the search has only to match speech that sounds like the predefined key words or phrases. Phonetic searches offer the flexibility of mining words, phrases, or proper names that are not already listed in the dictionary database.
Phonetics-based audio mining tends to deliver results with accuracy from 80 percent up to 98 percent.In the past several years, call centers have become interested in these technologies, although about 43 percent of organizations do not yet know what speech analytics really is or have any idea how it can benefit their business. Many centers have struggled to develop consistent, robust monitoring processes that include formal feedback and coaching sessions with agents. One of the most common reasons for poor performance in this area is because the demand on supervisor time is too great in other operational areas. Some call center managers began to view audio-mining as a way to better use the limited time resources of their supervisors by having the technology identify which calls should be monitored, such as calls in which a reservation is booked, a complaint is lodged, or a cancellation is requested. Until recently, however, these solutions had not taken hold in the call center market. Enter speech analytics.
Evolution of Speech Analytics TechnologiesSpeech analytics includes the audio-mining technologies described above but typically refers to a broader range of speech products, such as speaker identification, emotion detection, and talk analysis. Speaker identification in combination with audio-mining highlights specific items call center managers are trying to focus on, such as anytime a customer tries to cancel a reservation, close an account, or file a complaint; when an agent does not offer a greeting or close to the call; or when an agent neglects to cite required phrases for legal compliance purposes.Emotion detection can alert a supervisor or manager if a customer begins to get upset or agitated with the agent. Talk analysis can identify patterns within calls, such as long hold times or periods of silence, as well as the frequency of an agent cutting off a caller. Speech analytics can be used to research positive trends as well, such as when an agent presents a new program effectively or a caller is thrilled with the service they received. For example, Federal Express launched a program to identify instances in which customers provided a 'wow' response to the service they received.The use of emotion detection in speech analytics tools has been extended to deliver analysis to agents in real time. This new capability enables agents, supervisors, and quality specialists to get a live analysis of the choice of words or phrases the customer uses on a call, alerting them to a growing client sense of irritation, desperation, anger, and other emotions.Traditional speech analytics solutions allow organizations to search for calls by keyword, phrase, or business category, helping users find relevant conversations quickly to determine the underlying causes of rising call volumes, costs, and customer dissatisfaction. A new generation of speech analytics can now help automatically identify changes in customer behavior using technology such as Customer Behavior Indicators.
These next-generation solutions can proactively index every single word and phrase and can create a baseline of all dialogs that occur within customer interactions. This capability automatically surfaces the increases/decreases in the use of terms and phrases that may reflect a new potential trend, without the need to predefine terms in advance.In conjunction with other call center technologies, such as Integrated Voice Response (IVR) systems, speech analysis tools can help classify call types by determining the root cause of the call, which can identify trends not readily apparent to supervisors performing simple random call monitoring. Management analyses and response to developing trends promote tactical changes to reduce calls and customer complaints in cases of defective products or drive revenue when competitors are sold out or have increased prices.Increasingly, speech analytics is being deployed to share the structured data derived from raw, unstructured customer interactions with various business disciplines, such as marketing, sales, product development, and manufacturing, to refine the approach to rectifying core business issues and for targeting key product improvements. Executives can examine the 80 percent of complaints that come into call centers that are not related to agent performance to facilitate strategic planning processes. As more companies pursue Big Data solutions, they will turn to analytics to get a handle on the meaning and trends in all the data they collect.
Speech analytics is expected to play a large role in the Big Data boom.Speech analytics solutions are now going beyond call data, delving into interactions across multiple channels. Customer contact via email, text, online chat, Skype, Twitter, Facebook, and other social media sites can now be cataloged and analyzed to provide meaningful and actionable intelligence to the business.Speech analytics solutions have been purported to aid in many business functions, chief among them being root-cause analysis.